Research involving management of carbon dioxide has increased markedly over the last decade as it relates to concerns over climate change. Capturing and storing carbon dioxide (CO 2 ) in geological formations is one of many proposed methods to manage, and likely reduce, CO 2 emissions from burning fossil fuels in the electricity sector. Saline formations represent a vast storage resource, and the waters they contain could be managed for beneficial use. To address this issue, a methodology was developed to test the feasibility of linking coal-fired power plants, deep saline formations for CO 2 storage, and extracting and treating saline waters for use as power plant cooling water. An illustrative hypothetical case study examines a representative power plant and saline formation in the south-western United States. A regional assessment methodology includes analysis of injectioninduced changes in subsurface groundwater chemistry and fate and transport of supercritical CO 2 . Initial water-CO 2 -formation reactions include dissolution of carbonate minerals as expected, and suggest that very little CO 2 will be stored in mineral form within the first few centuries. Reservoir simulations provide direct input into a systems-level economic model, and demonstrate how water extraction can help manage injection-induced overpressure. Options for treatment of extracted water vary depending upon site specific chemistry. A high efficiency reverse osmosis system (HERO TM ) shows promise for economical desalination at the volumes of recovered water under consideration. Results indicate a coupled use CO 2 storage and water extraction and treatment system may be feasible for tens to hundreds of years.

A total of 18 wells, 14 producers and 4 injection wells, were drilled and completed during the Field Demonstration portion of the project. These 18 wells are all currently in service, with the producing wells going on-line between May and September 1996, and the injection wells going into service between August and December 1996. Current Unit production is approximately 3,100 BOPD, of which approximately 800 BOPD is being contributed from the 14 Project 10-acre producing wells (Figure ). A revision in the Statement of Work was approved to allow for the drilling of additional 10-acre infill wells or injection well conversions as budget constraints allow. A total of 2,730 feet of core was taken from four of the Project wells during the Field Demonstration portion of the Project. This core was taken to the Fina core facility in Midland, Texas and studied and described by a Fina geologic team, headed up by the Project Geologist, Brian Pregger. The core was originally scheduled to be cut continuously through the entire

The performance evaluation of Peng-Robinson Equation of State to satisfactorily simulate constant volume depletion experimental data of gas condensate reservoir fluid system has been presented. Constant volume depletion experiment was performed on a representative gas condensate reservoir fluid sample and the experimental data simulated using tuned Peng-Robinson equation of state on ECLIPSE PVT PRO simulator. Both experimental and simulated results were compared in terms of dew-point pressure, gas density, gas viscosity, gas compressibility factor, and gas formation volume factor. The results show that the simulation results exhibited similar trends as the experimental results. The dew-point pressure from the laboratory data is 4724 psia, while that of the simulation result is 4726.61 psia. This gave an average absolute deviation of 0.2092 %, a satisfactory match to the experimental data. The results presents a quantitative measure of the comparison of simulated and experimental constant volume depletion test, which quantitatively illustrates the capability of equations of state to satisfactorily simulate the constant volume depletion experimental test data -a somewhat contribution to existing body of knowledge.

Sand production with reservoir fluid is a well known problem in many parts of the world. Sand production in a well causes restricted production or complete loss of production due to sanding of well bore. There are several methods of sand control but most of the technologies require heavy hardware and tools thus making the cost of new well very high. Chemical sand consolidation can be an alternative to traditional techniques for poorly consolidated formations. The present studies are carried out to develop a suitable sand control technique for weakly consolidated oil/gas reservoirs using indigenously developed chemicals. The concept is based on the fact that water soluble polymers/chemicals have a strong tendency to adsorb on the surface of rock, thus binding the loose sand. However, the permeability of the formation is maintained by injecting oil soon after injection of chemicals. In order to develop this technology, a laboratory study was carried out to evaluate the chemicals for s...

Although nowadays finite difference simulators have many advantages for reservoir engineers to predict reservoirs in three dimensions, they have some disadvantages, such as spending much run time for complex fields with several thousand grids, whichby using streamline simulationthe run time was decreased considerably. In this paper, first a brief summary of streamline simulation and its uniqueness applications were discussed and then an Iranian oil reservoir as a case study was simulated by using FrontSim streamline simulator. Then, besides testing the results of Sayyafzadeh et al. (2010) to find the location of infill wells, the most suitable locations for infill drilling were selected. After that, vertical and horizontal infill wells were investigated and overall 34 scenarios were tested by using the streamline simulation method and FrontSim software. Finally the results of simulation and testing infill drilling scenario show significant increase in field oil production and field oil recovery factor, approximately 13%, and also a reduction in field water cut. In addition, distinguishing fractures or faults, well's drainage area, and understanding how much injectors affect the field oil production are some of the other benefits of the streamline simulation method. The novelty of this paper was to study the effects of horizontal infill wells and to compare the results between horizontal and vertical infill drilling using streamline simulation.

The main objective of this study is to provide a comprehensive investigation into the effect of Capillary number (N Ca) on production from the lean gas condensate reservoirs and to show the importance of N Ca in these types of reservoirs. In the current study a gas condensate reservoir has been simulated and validated using field and PVT data. The effect of N Ca on the parameters such as producing gas flow rate, bottom-hole pressure, average reservoir pressure, the amount of condensate in the reservoir and the condensate saturation versus the distance from the production well has been investigated. It was found that N Ca can increase the relative permeability of the gas followed by the reduction in condensate formation and pressure drop in the vicinity of wellbore. Furthermore, the results stated that N Ca exists in about 10 ft away from the well and affects mainly the well productivity parameters such as bottom-hole pressure and gas flow rate. However, it does not affect the average reservoir pressure and condensate throughout the reservoir as much as the near wellbore parameters.

Early shutting time in production wells due to water production performs an important role to determine production efficiency and useful life of the reservoir. In this study, in order to postpone the shut-in time of producing wells, increase oil displacement, and enhance production efficiency, production and injection wells capabilities with respect to their position in the reservoir were studied by using the concept of streamline. In the oil reservoirs, increasing injection flow rate does not necessarily enhance oil displacement and recovery. Therefore, suitable injection rates according to injection and production wells position have to be optimized. Also, production wells flow rate can affect sweep efficiency optimization extremely and increase the efficiency of injection wells. In this study, according to the position of production and injection wells and water production rates resulting from injection wells, four scenarios with different injection and production rates were investigated. This optimization has led to a reduced water production and water injection. Also, it increased the production efficiency and reservoir life.

Production from gas condensate reservoirs requires precise determination of reservoir fluid properties along with their positive impact on real reservoir performance evaluation and fluid in place volume
calculation. This fact is particularly important because liquid drop out phenomena occurs as a result of pressure drop due to fluid production and condensate remains in reservoir [Mohebzadeh, 2005. In this
study the challenges and problems of fluid phase behavior simulation in southern Iranian retrograde gas condensate reservoirs is discussed. Most of the problems and challenges are inaccuracy of equation of
states near the critical point, special conditions and produced fluid flow constraints, sampling pressure and reservoir pressure during the sampling process, problems related to the constant composition expansion and constant volume depletion experiments, inefficiency in plus fraction characterization,problems arising from lack of the swelling test, and finally C7+ characterization.

Simulation problems encountered in reservoir management are often computationally expensive because of the complex fluid physics for multiphase flow and the large number of grid cells required to honor geological heterogeneity. Multiscale methods have been proposed as a computationally inexpensive alternative to traditional fine-scale solvers for computing conservative approximations of the pressure and velocity fields on high-resolution geo-cellular models. Although a wide variety of such multiscale methods have been discussed in the literature, these methods have not yet seen widespread use in industry. One reason may be that no method has been presented so far that handles the combination of realistic flow physics and industrystandard grid formats in their full complexity. Herein, we present a multiscale method that handles both the most wide-spread type of flow physics (black-oil type models) and standard grid formats like corner-point, stair-stepped, PEBI, as well as general unstructured, polyhedral grids. Our approach is based on a finite-volume formulation in which the basis functions are constructed using restricted smoothing to effectively capture the local features of the permeability. The method can also easily be formulated for other types of flow models, provided one has a reliable (iterative) solution strategy that computes flow and transport in separate steps. The proposed method is implemented as open-source software and validated on a number of two and three-phase test cases with significant compressibility and gas dissolution. The test cases include both synthetic models and models of real fields with complex wells, faults, and inactive and degenerate cells. Through a prescribed tolerance, the solver can be set to either converge to a sequential or the fully implicit solution, in both cases with a significant speedup compared to a fine-scale multigrid solver. Altogether, this ensures that one can easily and systematically trade accuracy for efficiency, or vice versa.

The present work describes a fully implicit simulator for polymer injection implemented in the free, open-source MATLAB Reservoir Simulation Toolbox (MRST). Polymer injection is one of the widely used enhanced oil recovery (EOR) techniques and complicated physical process is involved, which makes accurate simulation very challenging. The proposed work is intended for providing a powerful and flexible tool to investigate the polymer injection process in realistic reservoir scenarios. Within the model, the polymer component is assumed to be only transported in the water phase and adsorbed in the rock. The hydrocarbon phases are not influenced by the polymer and they are described with the standard, three-phase, black-oil equations. The effects of the polymer are simulated based on the Todd-Longstaff mixing model, accounting for adsorption, inaccessible pore space, and permeability reduction effects. Shear-thinning/thickening effects based on shear rate are also included by the means of a separate inner-Newton iteration process within the global nonlinear iteration. The implementation is based on the automatic differentiation framework in MRST (MRST-AD), and an iterative linear solver with a constrained pressure residual (CPR) preconditioner is used to solve the resulting linear systems efficiently. We discuss certain implementation details to show how convenient it is to use the existing functionality in MRST to develop an accurate and efficient polymer flooding simulator for real fields. With its modular design, vectorized implementation, support for stratigraphic and general unstructured grids, and automatic differentiation framework, MRST is a very powerful prototyping and experimentation platform for development of new reservoir simulators.

Upscaling of parameters involved in single and two-phase flow has been researched quite extensively, and several methods for performing upscaling are known and understood. Less work has been done related to upscaling of enhanced oil recovery simulations. This is what we investigate, and in particular, we consider upscaling of parameters related to polymer flooding, which is the process in which large polymer molecules are added to the injected water to enhance its ability to push hydrocarbons through the reservoir. Herein, the polymer flooding process is described as a twophase, immiscible system that in addition to a Todd-Longstaff mixing model includes permeability reduction, polymer adsorption, and dead pore space. Effective parameters are computed by running simulations until a steady-state is reached and then performing upscaling based on the fluxes. This method is used by a major oil company as part of an established work flow for single and two-phase upscaling, and it is therefore natural to try to extend the method to polymer flooding. The upscaling is performed on the meter scale, where the steady-state assumption best can be justified. The procedure involves first performing singlephase upscaling of the absolute permeability, then two-phase upscaling of relative permeabilities, and finally, upscaling of the parameters involved in polymer flooding. The new upscaling method is verified against an analytical solution and validated on two synthetic models that include real data. Results show that the permeability reduction factor, which only depends on polymer concentration in the fine-scale model, will generally also depend on water saturation in the upscaled model. This introduces addition computational costs in the simulation, since the property evaluations now require extensive use of lookup-tables and interpolation. We therefore suggest making simplifications in order to reduce the complexity.

Multiscale solution methods are currently under active investigation for the simulation of subsurface flow in heterogeneous formations. These procedures capture the effects of fine-scale permeability variations through the calculation of specialized coarse-scale basis functions. Most of the multiscale techniques presented to date address subgrid capturing in pressure solution (elliptic/parabolic equations). In this paper we propose a multiscale method for solving transport equations on a coarse grid. In this method the global flow is computed on a coarse grid scale, but information from a fine scale velocity field is used to improve accuracy. The method is applied to incompressible and immiscible two-phase flow on a synthetic geological model with corner-point grid geometry. Although corner-point grids are given on a logically Cartesian format, the resulting grids are unstructured in physical space. The numerical results demonstrate that the multiscale method gives nearly the same flow characteristics as simulations where the transport equation is solved on the scale of an underlying fine grid.

We have developed a parallel and distributed computing framework to solve an inverse problem, which involves massive data sets and is of great importance to petroleum industry. A Monte Carlo method, combined with proxies to avoid excessive data processing, is employed to identify reservoir simulation models that best match the oilfield production history. Subsequently, the selected models are used to forecast future productions with uncertainty estimates. The parallelization framework combines: (1) message passing for tightly coupled intra-simulation decomposition; and (2) scheduler/Grid remote procedure calls for model parameter sweeps. A preliminary numerical test has included 3,159 simulations on a 256-processor Intel K.-I. Nomura ( ) Collaboratory for Advanced Computing and Simulations (CACS),

The reinjection of sour or acid gas mixtures is often required for the exploitation of hydrocarbon reservoirs containing remarkable amounts of acid gases (H 2 S and CO 2 ) to reduce the environmental impact of field exploitation and provide pressure support for enhanced oil recovery (EOR) purposes. Sour and acid gas injection in geological structures can be modelled with TMGAS, a new Equation of State (EOS) module for the TOUGH2 reservoir simulator. TMGAS can simulate the two-phase behaviour of NaCl-dominated brines in equilibrium with a non-aqueous (NA) phase, made up of inorganic gases such as CO 2 and H 2 S and hydrocarbons (pure as well as pseudo-components), up to the high pressures (∼100 MPa) and temperatures (∼200 • C) found in deep sedimentary basins. This study is focused on the near-wellbore processes driven by the injection of an acid gas mixture in a hypothetical high-pressure, under-saturated sour oil reservoir at a well-sector scale and at conditions for which the injected gas is fully miscible with the oil. Relevant-coupled processes are simulated, including the displacement of oil originally in place, the evaporation of connate brine, the salt concentration and consequent halite precipitation, as well as non-isothermal effects generated by the injection of the acid gas mixture at temperatures lower than initial reservoir temperature. Non-isothermal effects are studied by modelling in a coupled way wellbore and reservoir flow with a modified version of the TOUGH2 reservoir simulator. The described approach is limited to single-phase wellbore flow conditions occurring when injecting sour, acid or greenhouse gas mixtures in high-pressure geological structures.

An essential task that connects hydrocarbon estimation and operational decision-making especially for oilfields in Africa is the forecast of oil and gas flow rates. The reason for the focus on African oilfields is partly due to the rising concerns of poor metering of oil wells in the continent and partly due to the difficulties oil producers in the region face as a result of the lack of a universal model and scarcity of data. The methods, data sources, and difficulties of estimating oil and gas flow rates in this situation are discussed in this paper. To estimate flow rates based on reservoir features and production factors, numerous prediction models, including empirical, analytical, and numerical techniques, have been developed. To improve prediction accuracy, these models frequently include geological, geophysical, and engineering data. The prediction process, however, is rife with uncertainties brought on by complex geology, heterogeneity in the reservoir, fluid behaviour, and technical restrictions. Improving the accuracy of flow rate projections and the ensuing reservoir management techniques requires addressing these uncertainties. To improve prediction accuracy and decision-making, advanced data analytics, machine learning, and uncertainty quantification methodologies can be integrated. This paper emphasizes the value of high-quality data integration and data collecting to improve model correctness. Additionally, it underlines the significance of including uncertainty in projections and provides guidance on how decision-makers can make informed choices while taking into account probable changes in expected flow rates. Stakeholders in the oil and gas sector may improve production methods, reduce operational risks, and support the sustainable development of energy resources across the African continent by addressing these crucial factors.

This work shows a procedure to build fast and reliable numerical models with WAG-CO2-rich injection scheme. This novel and practical approach to numerical tuning high-complexity reservoir models can save days or even months of work. Improving step 2 of the 12-step reservoir characterization and modeling methodology proposed by Schiozer et al. (2015) leads to an optimization of the numerical control of the model based on the critical compositional numerical parameters and performance diagnostics. We show the results of a probabilistic risk analysis application. For the complex case scenario presented, results show that applying the proposed technique can save roughly 80% of the total time spent to perform a risk study. Furthermore, we found that time saving tends to increase as the number of simulations increases. This work improvement comes from making a methodology that includes both compositional and black-oil numerical solver parameters in every step of the numerical tuning optim...

The petroleum industry uses high level dynamic simulations applied to geocellular models to guide forecasts of oil, gas and water production. Uncertainty in model choice and input variable selection is often addressed through large numbers of computationally slow Monte Carlo simulations designed around physics based models. Here, an alternate approach is proposed, which uses a relatively small amount of data and a reduced number of simulations of the high level physics model to train a fast (to evaluate) proxy or surrogate model based on a Polynomial Chaos Expansion. We give details of the theory and incorporated techniques, which significantly increase flexibility. Input variables (e.g. cell-by-cell variations in porosity and permeability) are sampled from unknown probability distributions and sensitivity analysis is based on low level proxy models. The theory is tested by developing proxy models to predict total gas production from a five-spot well configuration in the Hermitage a...

Producing oil from gas-lift wells are often faced with severe producing oscillatory flow regimes. A major source of the oscillations is recognized as casing-heading instability which is caused by dynamic interaction between injection gas and multiphase fluid. This phenomenon poses strict productionrelated challenges in terms of lower average production and strain on downstream equipment. In this paper, an effective solution is proposed based on integration of an online interpretation dynamic model and a nonlinear model predictive control (NMPC) scheme. The paper uses adaptive growing and pruning radial basis function (GAP-RBF) neural networks (NNs) to recursively capture the essential dynamics of casing-heading instability in a nonlinear model structure. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) are comparatively investigated to adaptively train modified GAP-RBF NNs. NMPC methodology is developed on the basis of the identified nonlinear NN model for real-time stabilization of casing-heading instability in an oil reservoir equipped with a gas-lift production well. A set of test studies has been conducted to explore the superior performance of the proposed adaptive NMPC controller under different scenarios for an oil reservoir simulated in ECLIPSE and linked to a complementary gas-lifted oil well simulated in programming environment.

Advances in asphaltene science and a new generation of downhole fluid analysis (DFA) technology have been combined to yield powerful new insights to reservoir tar mats. The asphaltene nanoscience model, the modified Yen model, also known as the Yen-Mullins model, has enabled development of the industry's first predictive equation of state for asphaltene concentration gradients. This equation of state (EOS) is a modified Flory-Huggins regular solution model for the asphaltene part that has been referred to as the Flory-Huggins-Zuo (FHZ) EOS for asphaltene concentration gradients in oil reservoirs. Measurement of these gradients using "downhole fluid analysis" coupled with analysis using the FHZ EOS has successfully addressed a variety of reservoir concerns including reservoir connectivity, viscosity gradients, and fluid disequilibrium. The EOS model shows that asphaltene concentration gradients can be large owing to both the gravity term and gas/oil ratio (GOR) gradients. The FHZ EOS is reduced to a very simple formthe gravity term only for low GOR black oils and heavy oilsand heavy oils are shown to exhibit enormous asphaltene concentration gradients in contrast to predictions from conventional models. In this paper, the FHZ EOS has been applied not only to calculate asphaltene concentration gradients but also to predict asphaltene phase instability in oil reservoirs. Two types of tar mats are discussed: one with a large discontinuous increase in asphaltene concentration versus depth typically at the base of an oil column (corresponding to asphaltene phase transition); the second with a continuous increase in asphaltene content at the base of a heavy oil column due to an exponential increase in viscosity with asphaltene content. Both types of tar mats are consistent with the Yen-Mullins model of asphaltenes within the FHZ EOS analysis discussed herein. The predictions are in good agreement with the laboratory and field observations, and the mechanisms of forming these two kinds of tar mats are also discussed. This methodology establishes a powerful new approach for conducting the analyses of asphaltene concentration grading and tar mat formation in oil reservoirs by integrating the Yen-Mullins model and the FHZ EOS with DFA technology.

This study demonstrates how machine learning is used to quantify reservoir property changes from 4D seismic. We apply an end-to-end machine learning workflow to deliver saturation and pressure changes in reservoir with time. We screened and categorized the sources impacting seismic attributes to understand their influences on the final prediction of pressure and saturation changes. In the Gulf of Mexico (GOM) case study, we utilized a synthetic seismic as the training dataset, generated from a history-matched simulation model and a calibrated rock physics model. The synthetic seismic provided direct estimations of the input features' quality and uncertainty ranges. Finally, we applied an iterative approach to estimate changes in the water saturation, gas saturation and pore pressure of the reservoir. The iterative approach compares the predicted outputs against production, injection and pressure data gathered at the producers and the injectors creating a closed feedback loop. The proposed ML workflow can be applied to other fields with 4D seismic data after calibrating 4D synthetic seismic with a known rock physics model.

A numerical geothermal reservoir simulator is used in a series of calculations to help devise interpretation techniques for analyzing pressure transient data from two phase geothermal reservoirs. The investigations include (1) the drawdown/buildup behavior of initially two‐phase (liquid water/steam) reservoirs, (2) the drawdown/buildup response of initially single‐phase reservoirs which undergo flashing as a result of fluid production, and (3) the effects of cold water injection into hot two‐phase reservoirs. Production (drawdown/buildup) data from two‐phase (either initially or due to production‐induced boiling) reservoirs may be analyzed in the usual manner to yield kinematic mobility; buildup data from reservoirs which undergo flashing on production also offer the possibility of determining absolute formation permeability. Injection data can be interpreted in a straightforward manner to yield absolute formation permeability; the corresponding falloff data appear, however, to be o...

In the numerical simulation of underground reservoir behavior it is often necessary to employ well blocks (i.e., a grid block containing a well) with dimensions much larger than the well diameter. This paper develops practical procedures for making reasonable estimates of well bore sandface conditions from the well block conditions calculated by numerical reservoir simulations. It is assumed that on a sub‐grid scale, pressures will equilibrate much more rapidly than in the reservoir as a whole; the subgrid flow may therefore be treated as steady flow. The conditions at the sandface may then be deduced from mean well block conditions by assuming that the latter obtain at a definite distance (the ‘effective well block radius’) from the well. The problem is to establish the value of the effective well block radius in terms of the size of the well block and other relevant parameters. Grid pressures obtained in the numerical solution of liquid flow into a single well located in both radi...

In this paper, we introduce a mathematical model to describe the nanoparticles transport carried by a two-phase flow in a porous medium including gravity, capillary forces and Brownian diffusion. Nonlinear iterative IMPES scheme is used to solve the flow equation, and saturation and pressure are calculated at the current iteration step and then the transport equation is soved implicitly. Therefore, once the nanoparticles concentration is computed, the two equations of volume of the nanoparticles available on the pore surfaces and the volume of the nanoparticles entrapped in pore throats are solved implicitly. The porosity and the permeability variations are updated at each time step after each iteration loop. Two numerical examples, namely, regular heterogeneous permeability and random permeability are considered. We monitor the changing of the fluid and solid properties due to adding the nanoparticles. Variation of water saturation, water pressure, nanoparticles concentration and p...

This paper reviews a variety of derived single-purpose operating policies for reservoirs in series and in parallel for water supply, flood control, hydropower, water quality, and recreation. Such rules are useful for real-time operations, conducting reservoir simulation studies for real-time, seasonal, and long-term operations, and for understanding the workings of multireservoir systems. For reservoirs in series, several additional new policies are derived for special cases of optimal short-term operation for hydropower production and energy storage. For reservoirs in parallel, additional new special-case rules are derived for water quality, water supply, and hydropower production. New operating policies also are derived for reservoir recreation.

In this work, the main phenomena of genesis and hydrocarbons migration in a sedimentary basin are presented. Then, a mathematical modelling of these characters is formulated. Pressure, hydrocarbons masses and water mass are the main unknowns which have held our attention for mathematical modelling. Phases miscibilities effects are also taken into account. Afterwards a numerical method to compute the mathematical model is described. The numerical method must be robust, stable and low cost in computing time. At last, numerical results are exposed in a black-oil flow case.

Simulation of multiphase flow systems are of critical importance in managing hydrological systems. Flow simulations are affected by a number of factors including structure and flow properties including porosity and permeability as well as the anisotropy and heterogeneity of these properties. In many cases traditional hydrological and reservoir data are highly affected by these parameters, but are not directly sensitive to them. As such modellers often adjust these parameters in an ad hoc manner until solutions numerically converge. Simulation models are generally based on structural data from reflection seismics whose physical flow properties are then populated using geostatistical extrapolation techniques utilizing a sparse number of borehole logs and core analysis. In multiphase systems including enhanced oil recovery and carbon capture and sequestration uncertainties regarding phase-dependent physical properties confounds this challenge further. Geophysical methods provide a means by which to gain an improved understanding of phase distributions in the subsurface. In this paper we will look at applications from active carbon capture and sequestration and enhanced oil recovery applications, as well as synthetic examples. Geophysical data including electromagnetic and gravity are inverted using structural constraints from the reservoir model. Inversions are then mapped into flow properties using calibrated relations such as Archie's Equation. The coupled models can then be used to both verify and improve on the reservoir flow model which improves it's predictive power and utility as a management tool.

Optimal injector selection is a key oilfield development endeavor that can be computationally costly. Methods proposed in the literature to reduce the number of function evaluations are often designed for pattern level analysis and do not scale easily to full field analysis. These methods are rarely applied to both water and miscible gas floods with carbon storage objectives; reservoir management decision making under geological uncertainty is also relatively underexplored. In this work, several innovations are proposed to efficiently determine the optimal injector location under geological uncertainty. A geomodel ensemble is prepared in order to capture the range of geological uncertainty. In these models, the reservoir is divided into multiple well regions that are delineated through spatial clustering. Streamline simulation results are used to train a meta-learner proxy. A posterior sampling algorithm evaluates injector locations across multiple geological realizations. The proposed methodology was applied to a producing field in Asia. The proxy predicted optimal injector locations for water and CO 2 EOR and storage floods within several seconds (94-98% R 2 scores). Blind tests with geomodels not used in training yielded accuracies greater than 90% (R 2 scores). Posterior sampling selected optimal injection locations within minutes compared to hours using numerical simulation. This methodology enabled the rapid evaluation of injector well location for a variety of flood projects. This will aid reservoir managers to rapidly make field development decisions for field scale injection and storage projects under geological uncertainty.

Optimal injector selection is a key oilfield development endeavor that can be computationally costly. Methods proposed in the literature to reduce the number of function evaluations are often designed for pattern level analysis and do not scale easily to full field analysis. These methods are rarely applied to both water and miscible gas floods with carbon storage objectives; reservoir management decision making under geological uncertainty is also relatively underexplored. In this work, several innovations are proposed to efficiently determine the optimal injector location under geological uncertainty. A geomodel ensemble is prepared in order to capture the range of geological uncertainty. In these models, the reservoir is divided into multiple well regions that are delineated through spatial clustering. Streamline simulation results are used to train a meta-learner proxy. A posterior sampling algorithm evaluates injector locations across multiple geological realizations. The propo...

Homotopy analytical method (HAM) is a computational method that provides solution to highly non-linear partial differential equations (PDE's) in much similar way as other perturbation and asymptotic techniques. This research work illustrates the benefit of using (HAM) to obtain a type curve over conventional log approximation of dimensionless variables. From such type curves, average reservoir properties, porosity and permeability were obtained from dimensionless equations. This type curve was obtained by matching plots from HAM solution and well test data. The HAM is independent of small/large parameter and can be used to obtain analytical solutions to highly nonlinear partial differential equations unlike other analytical and asymptotic techniques applies to weakly nonlinear problems. The HAM solution gives values of reservoir pressure distribution as a continuous function of time and converges easily for large reservoirs (rD =380) typically. One advantage of HAM is that it provides a means to ensure convergence of series solution by introduction of convergent control parameter. Plots of reservoir pressure distribution as a function of time curves were also obtained and extrapolated for easy matching and were matched with the type curve. Dimensionless pressure and time were used to compute average porosity and permeability which were found to be 1.51md permeability and 1.81% porosity. Computer program was developed for computations using MATLAB and further analysis of data using EXCEL. From the match points obtained, reservoir properties were estimated.

Summary This paper presents a gridding study relating to reservoir simulation of a giant, complex, low-permeability carbonate reservoir developed with 75 ultraong horizontal wells in a densely spaced alternating injector/producer pattern. The lateral magnitude of the Al Shaheen field in Qatar and the radial layout of the multiple ultralong horizontal wells in the field posed a challenge in modeling of individual well performance using a manageable grid size with an acceptable run time for history matching. Reservoir modeling was complicated further by the complex reservoir characteristics with a tilting free-water level (FWL), separate gas caps, large lateral variations in oil properties, and wettability-dependent flow characteristics. These features had to be incorporated into the initialization and dynamic modeling of the reservoir, which added further to the memory requirements of the simulation model. This paper describes the process of selecting a suitable simulation grid for h...

In this research the experimental and theoretical studies on different Enhanced Oil Recovery (EOR) techniques, i.e. Water Flooding (WF), Gas Injection (GI) and Water Alternating Gas process (WAG) were performed on specimens taken from an Iranian carbonate offshore reservoir at the reservoir condition. The experimental results for each specified techniques were compared with the corresponding results obtained from a simulation model. In the case of WF and GI, the injection rates were set to be 0.1, 0.2 and 0.5 cc/min while for the WAG experiments, with two WAG ratios 1 and 2 and with 7, 7, and 10 cycles, the injection rates were 0.1, 0.2 and 0.5 cc/min. The results obtained from the experiments revealed that in all cases the amount of recovered oil is increased. Furthermore, the results showed that increase in the recovery of oil is significant in the case of the WAG injection with optimum rate of injection fluids comparing to those of the WF and GI scenarios. It was also pronounced that the recovery of oil with WAG ratio 2 is more than that with ratio 1. It should be mentioned that samples for sea water and pure methane were considered to be as injection fluids. It was also shown that the experimental results can be accurately correlated with a black oil numerical simulator, Eclipse100.

This work presents a qualitative analysis of the uncertainties in perfonnance prediction for a reservoir characterization process in which static and dynamic data are combined. Using primary, singlephase production data as the dynamic constraint in the inversion, we evaluate the quality of the models when they are used in alternative or subsequent production scenarios. We conclude that, provided the well configuration remains the same as that used for the dynamic constraint in the inversion, the models perfonn well when the production stage remains single-phase, primary depletion, even at the individual well level and for future prediction. For multiphase primary production, there is also a reasonable match in perfonnance, although not as good as for a single-phase case. This is because such depletion processes are controlled by the penneability in the nearwellbore region, which is adequately reconstituted by the characterization process used; we observed, however, that the multiphase density variations with time had little impact. However, the fine scale, inter-well penneability patterns of the different realizations obtained by the integration of the dynamic and static data are not sufficiently resolved and so are unable to capture adequately the inter-well penneability heterogeneity patterns that are necessary for accurate waterflood perfonnance matching. Thus, the models produced by inversion in which primary recovery data are used as a constraint are inadequate in predicting the perfonnance of processes involving injection.

Summary A user-friendly prototype expert system (UTINPUT) was developed for preparing input data sets to design miscible gasfloods with the U. of Texas Compositional Simulator (UTCOMP). The expertise we acquired through literature, simulation experts, and simulation studies was implemented in an expert-system shell with rules, objects, and C.

Fractured and vuggy carbonate reservoirs present a complex storage space with irregularly distributed fractures and caves. Furthermore, these reservoirs typically feature the presence of a substantial bottom aquifer, further complicating the fluid flow dynamics. At present, most well test models for this reservoir are based on discrete media primarily address single-phase flow scenarios, typically considering caves as equipotential bodies. This approach cannot accurately represent the complexities of such reservoirs. In this paper, a three-dimensional numerical well test model for two-phase oil-water flow within fractured and vuggy carbonate reservoirs is introduced. Randomly generated natural fractures are embedded within the reservoir, and the Hagen-Poiseuille law is utilized to describe fluid flow within cave spaces, effectively coupling flow interactions across fractures, caves and the porous rock matrix. The computational domain is discretized by a perpendicular bisection grid, and the finite volume method is used to solve the model, allowing for the calculation of the pressure and saturation fields at each time step. Subsequently, well test type curves are constructed and analyzed, flow regimes are segmented, and sensitivity analysis of model parameters is conducted. The pressure buildup data from well A are interpreted, and the results demonstrate a remarkable agreement between the well test curve and actual data, confirming the capability of the model to capture reservoir characteristics and complex fluid flow phenomena. The findings lay the foundation for the development of numerical well test models tailored to fractured and vuggy carbonate reservoirs.

Liquid dropout and retention in gas-condensate reservoirs, specially in the near wellbore region, obstruct gas flowing paths and impact negatively the produced fluid volume and composition. Yet, condensate banking forecasting is commonly inaccurate, as experiments seldom reproduce reservoir extreme conditions and complex fluid composition, while most pore-scale models oversimplify the physics of phase transitions between gas and condensate. To address this gap, a fully implicit isothermal compositional pore-network model for gas and condensate flow is presented. The proposed pore-networks consist of 3D structures of pores connected by constricted circular capillaries. Hydraulic conductances are calculated for the capillaries, which can exhibit single-phase flow or two-phase annular flow, according to local gas and liquid saturations, or be blocked by a liquid bridge, when capillary forces overcome viscous forces. A PT-flash based on the Peng-Robinson EoS is performed at control volumes defined for the pores at each time step, updating the phases properties. Flow analyses were carried based on coreflooding experiments reported in the literature, with matching fluid composition and flow conditions, and approximated pore-space geometry. Predicted and measured relative permeability curves showed good quantitative agreement, for two values of interfacial tension and three values of gas flow velocity.

Liquid dropout and retention in gas-condensate reservoirs, specially in the near wellbore region, obstruct gas flowing paths and impact negatively the produced fluid volume and composition. Yet, condensate banking forecasting is commonly inaccurate, as experiments seldom reproduce reservoir extreme conditions and complex fluid composition, while most pore-scale models oversimplify the physics of phase transitions between gas and condensate. To address this gap, a fully implicit isothermal compositional pore-network model for gas and condensate flow is presented. The proposed pore-networks consist of 3D structures of pores connected by constricted circular capillaries. Hydraulic conductances are calculated for the capillaries, which can exhibit single-phase flow or two-phase annular flow, according to local gas and liquid saturations, or be blocked by a liquid bridge, when capillary forces overcome viscous forces. A PT-flash based on the Peng-Robinson EoS is performed at control volu...

Leading to achieve net zero emissions, performing carbon capture and storage (CCS) on a large scale is becoming more necessary, especially for developing countries, which are highly affected by the continuously increasing release of carbon dioxide (CO2). It has also been observed that developing countries does not participate much in the release of CO2 in the atmosphere but are highly influenced by global warming because of geological location. Therefore, addressing challenges of climate changes and its impacts requires high-capacity storage in safe and reliable locations. Depleted oil and gas reservoirs offer a valuable option to store CO2 due to their adequate porosity and permeability. In this research, an effort has been made to provide a simulation study and comprehensive analysis of CO2 storage through reservoir simulation in subsurface oil reservoir. In contrast to prior works, this research article introduces a simulation approach to assess the feasibility of CO2 storage in an oil reservoir. Storage in an oil reservoir was modeled using a commercial compositional simulator. CO2 behavior during injection is examined using gas injection profiles throughout the injection duration and injection rate. Results of the study demonstrate that reservoir pressure changes equally in all layers and grid blocks making the evaluated reservoir suitable for CO2 storage. Bottom hole pressure (BHP) behavior during injection shows the feasibility of CO2 storage. The analysis revealed that continuous injection of CO2 at a rate of 3500 Mscf/day over a period of 10 years led to a successful storage scenario, with the reservoir reaching its space limit and the injection rate dropping to zero. These results suggest the viability and effectiveness of CO2 storage as a means of mitigating greenhouse gas (GHG) emissions.

An iterative multigrid solution is developed for solving large systems of fluid flow equations often encountered in petroleum reservoir engineering. The multigrid solver is coded into a parallelized version of a multi-phase, three-dimensional, black-oil reservoir simulator. Test problems from the petroleum engineering literature are studied to evaluate performance of the solver on the massively parallel Cray T3D. Results of simulations in the 4-, 8-, 16-... 64-processor environment indicate substantial performance gains offered by the Cray T3D and the potential application of this approach in solving large reservoir engineering problems.

SummaryOne of the outstanding challenges in reservoir characterization is to build high-resolution reservoir models that satisfy static as well as dynamic data. However, integration of dynamic data typically requires the solution of an inverse problem that can be computationally intensive and becomes practically infeasible for fine-scale reservoir models. A critical issue here is computation of sensitivity coefficients, the derivatives of dynamic production history with respect to model parameters such as permeability and porosity.We propose a new analytic technique that has several advantages over existing approaches. First, the method utilizes an extremely efficient three-dimensional multiphase streamline simulator as a forward model. Second, the parameter sensitivities are formulated in terms of one-dimensional integrals of analytic functions along the streamlines. Thus, the computation of sensitivities for all model parameters requires only a single simulation run to construct t...

Progressing cavity pump (PCP) systems are widely used in the oil and gas industry. Continuously evaluating PCP performance helps to maximize and sustain fluid production and increase pump run-life. This paper focuses on integrating a real-time platform and advanced software to model, troubleshoot, and optimize PCP systems and their operation.

More than 50% of installed PCP systems located in Great Burgan Field in southeast Kuwait are connected to a real-time SCADA platform. These connected systems are monitored to support daily operations and to identify underperforming wells for troubleshooting. Special attention is given to wells exhibiting critical behaviors or wells with optimization opportunities. Before implementing any actions on these wells, real-time data history is used along with nodal analysis to predict the outcomes. This paper presents an intensive optimization analysis through the following field case studies:

Preventing sucker rod string failure

Evaluating pump submergence

Optimizing fluid production

Identifying optimum operating conditions

Software is used to perform simulations of flow under different operating conditions and to generate a full analysis report based on PCP equipment configured in the well model. The sharp-edge results are not limited to the production rate. They also extend to pump performance and other surface and downhole parameters such as pump torque, intake pressure, and discharge pressure. The outcome of these results assists with making well-informed decisions with the following benefits:

Operating conditions have been improved by estimating the production rate at different speeds.

Pump life has been improved by evaluating rod load, lift load, and efficiency.

Down-time has been reduced by preventing pump-off conditions.

The procedure serves as a proven guide for analysis and optimization of PCP systems. Improving pump efficiency, achieving the target production rate, identifying problems, and preventing potential failures all help to optimize PCP system performance.

The innovative integration of PCP analysis and optimization provides a means to increase production and reduce the load percentage of surface and subsurface equipment parameters. A real-time SCADA platform combined with the optimization software created an ideal solution to keep wells operating at peak performance levels.

Prema objavljenim podacima o odabranom plinskom polju, predviđena je dinamika pridobivanja plina do 2025. godine. Pretpostavljajući ukupne količine proizvedenih fluida na pojedinim bušotinama, proračunat je i očekivani pad tlaka u ležištu. Temeljem ovih podataka izdvojena je tipska horizontalna bušotina, za postavljanje cjelokupnog proizvodnog modela bušotine u programu Prosper (Verzija 14.0, Sveučilišna licenca 4186). Na osnovu odgovarajućih ulaznih podataka, modeliranjem su određene proizvodne mogućnosti bušotine u podmorju. One prvenstveno uključuju proračun dinamičkog tlaka na dnu, odnosno indikatorsku krivulju, kao i proračun gradijenta pada tlaka u uzlaznim cijevima, čime je određena radna točka sustava. Cilj rada je usporediti točnost rezultata dobivenih temeljem dva različita modela za utok plina iz ležišta u horizontalnu bušotinu, prema autorima Goode i Kuchuk. Prvi slučaj proizvodnog modeliranja odnosi se općenito na ograničeno ležište (engl. Horizontal Well -No Flow Boundaries), a drugi uključuje i utjecaj trenja na dodatan pad tlaka (engl. Horizontal Well -dp Friction Loss in Wellbore).

Water injection is a core element of geothermal, petroleum and waste water management industries where water is injected into the subsurface after treatment and filtration to comply with environmental regulations and water quality requirements. Some impurities still remain in the water even after the filtration process. It has been widely reported that water injection operations encounter severe injectivity decline which is of substantial concern for field management. Total dissolved and suspended solids in injection water are one of the significant reasons for injectivity decline. These solids are then filtered by the porous media and cause formation damage which can result in significant injectivity decline. Major past studies have assumed typical filtration techniques (micro-filtration) where the size of remaining dispersed particles in the water are reduced to ~2 microns. Hence, the majority of the experimental studies have used suspended particles of ~1-5 microns to investigate...

Wet gas reservoirs show a complex behavior when they are produced below dew point pressure, according to appearance of a condensate banking phenomena and presence of a two-phase fluid in the reservoir. There is a pressure drop near the wellbore during producing gas from the reservoir to the surface. Due to excessive pressure drops, a condensate drops out from gas and flows with gas towards the wells because of compositional changes in PVT phase behavior. These all make a productivity of reservoir worse and reduce an overall recovery factor. In this study, a wet gas reservoir simulation has been conducted to provide more accurate model of the flow of gas in to the wells by generalizing pseudo-pressure method. Also this study presents how gas recycling method could increase the condensate production and finally by a sensitivity analyses the best time period of gas injection would be introduced for the under discussion reservoir.

Most hydrocarbon reservoirs contain faults, which are highly complex heterogeneous and anisotropic three-dimensional (3D) volumes of deformed rock. A major technical challenge in full-field flow simulation is to represent the effects of 3D fault zone structure within the two-dimensional (2D) fault planar surfaces using the industry standard commercial reservoir flow simulator due to its limited functionality. Therefore, a new flow-based geometrical upscaling (FBGU) method has been developed for capturing the effects of 3D fault zone structures in conventional low-resolution upscaled flow simulation models. Geometrical upscaling (GU) is the process of calculating across-fault and up-fault transmissibility arising from 3D flow paths through fault zones, and expressing these transmissibilities as implicit connections in a low-resolution upscaled flow simulation model. The high accuracy of the method is demonstrated by comparing the flow responses of high-resolution (referred as truth m...

Accurate and reliable optimization and simulation of the dam reservoir system to ensure optimal use of water resources cannot be achieved without precise and effective models. Providing insight into reservoir system operation and simulation modeling through a comprehensive overview of the previous studies and expanding research horizons can enhance the potential for accurate and well-designed models. The current research reviews previous studies that have used optimization methods to find optimal operating policies for a reservoir system over the past 20 years. Indeed, successful operating policies cannot be obtained without achieving accurate predictions of the main hydrological parameters in the reservoir system, which are inflow and evaporation. The present study focuses on giving an overview of the applications of AI-based models for predicting reservoir inflow and evaporation. The advantages and disadvantages of both optimization algorithms and predictive models have been summarized. Several recommendations for future research have also been included in the present review paper.

Umiat field, located in Alaska North Slope poses unique development challenges because of its remote location and permafrost within the reservoir. This hinders the field development, and further leads to a potential low expected oil recovery despite latest estimates of oil in-place volume of 1550 million barrels. The objective of this work is to assess various possible well patterns of the Umiat field development and perform a detailed parametric study to maximize oil recovery and minimize well costs using statistical methods. Design of Experiments (DoE) is implemented to design simulation runs for characterizing system behavior using the effect of certain critical parameters, such as well type, horizontal well length, well pattern geometry, and injection/production constraints on oil recovery. After carrying out simulation runs using a commercially available simulation software, well cost is estimated for each simulation case. Response Surface methodology (RSM) is used for optimization of well pattern parameters. The parameters, their interactions and response are modeled into a mathematical equation to maximize oil recovery and minimize well cost. Economics plays a key role in deciding the best well pattern for any field during the field development phase. Hence, while solving the optimization problem, well costs have been incorporated in the analysis. Thus, based on the results of the study performed on selected parameters, using interdependence of the above mentioned methodologies, optimum combinations of variables for maximizing oil recovery and minimizing well cost will be obtained. Additionally, reservoir level optimization assists in providing a much needed platform for solving the integrated production optimization problem involving parameters relevant at different levels, such as reservoir, wells and field. As a result, this optimum well pattern methodology will help ensure optimum oil recovery in the otherwise economically unattractive field and can provide significant insights into developing the field more efficiently. Computational algorithms are gaining popularity for solving optimization problems, as opposed to manual simulations. DoE is effective, simple to use and saves computational time, when compared to algorithms. Although, DoE has been used widely in the oil industry, its i application in domains like well pattern optimization is novel. This research presents a case study for the application of DoE and RSM to well optimization in a real existing field, considering all possible scenarios and variables. As a result, increase in estimated oil recovery is achieved within economical constraints through well pattern optimization.

Gas hydrates represent a huge potential future resource of natural gas. However, significant technical issues need to be resolved before this enormous resource can be considered to be an economically producible reserve. Developments in numerical reservoir simulations give useful information in predicting the technical and economic analysis of the hydrate-dissociation process. For this reason, a commercial reservoir simulator, CMG (Computer Modeling Group) STARS (Steam, Thermal, and Advanced Processes Reservoir Simulator) has been adapted in this study to model gas hydrate dissociation caused by several production mechanisms (depressurization, hot water injection and steam injection). Even though CMG is a commercially available simulator capable of handling thermal oil recovery processes, the novel approach of this work is the way by which the simulator was modified by formulating a kinetic and thermodynamic model to describe the hydrate decomposition. The simulator can calculate gas and water production rates from a well, and the profiles of pressure, temperature and saturation distributions in the formation for various operating conditions. Results indicate that a significant amount of gas can be produced from a hypothetical hydrate formation overlying a free gas accumulation by several different production scenarios. However, steam injection remarkably improves gas production over depressurization and hot water injection. A revised axisymmetric model for simulating gas production from hydrate decomposition in porous media by a depressurization method is also presented. Self-similar solutions are obtained for constant well pressure and fixed natural gas output. A comparison of these two boundary conditions at the well showed that a higher gas flow rate can be achieved in the long run in the case of constant well pressure over that of fixed gas output in spite of slower movement of the dissociation front. For different reservoir temperatures and various well boundary conditions, distributions of temperature and pressure profiles, as well as the gas flow rate in the hydrate zone and the gas zone, are evaluated.