Compositional Reservoir Simulation Research Articles

Response surface modeling of CO2dynamic storage efficiency factor in high permeability thick sandstones

AbstractIn order to utilize as much of the pore space for CO2storage in high permeability thick saline aquifers, it is vital to investigate the interactions of injected CO2with formation brine and rock. In order to quantify the displacement process, we investigate the dynamic storage efficiency factor (DSEF) for saline aquifers where pressure increase is minimal during the injection phase. Dimensionless numbers are derived from basic governing equations, constitutive equations, initial and boundary conditions using the inspection analysis. Then using the Hammersley sequence sampling, 178 numerical experiments are designed, and a compositional reservoir simulator is used to perform these simulations. In the next step, response surface regression analysis is used to establish a relationship between DSEF obtained from the numerical simulations and the corresponding dimensionless numbers. The simulation results show that for the studied conditions the underground dynamics is mostly influenced by the gravity number, followed by effective aspect ratio and dip numbers. The results from the response surface regression analysis are used to develop a correlation, which can be used to estimate the dynamic CO2storage capacity of relevant zones. This study provides quantitative measures for the different competing mechanisms involved in underground displacement of fluids in CO2geological storage, which can serve as a useful tool during planning phase of storage projects. © 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

AbstractNumerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large‐scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input‐output relations of physics‐based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC‐GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC‐GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC‐GAN is that it includes a self‐training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC‐GAN model is designed to learn cross‐domain mappings between high‐dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC‐GAN for predicting the migration of carbon dioxide (CO2) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC‐GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC‐GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC‐GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics‐based models used in both groundwater and subsurface energy exploration applications.

Work Flow Uses Vaca Muerta Data To Optimize Asset Evaluation

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 191272, “Optimization Under Uncertainty for Reliable Unconventional Play Evaluation: A Case Study in Vaca Muerta Shale Gas Blocks, Argentina,” by Reza Mehranfar, Leonardo Marquez, SPE, Raphael Altman, SPE, Hassan Kolivand, Rodrigo Orantes, and Oswaldo Espinola, SPE, Schlumberger, prepared for the 2018 SPE Trinidad and Tobago Section Energy Resources Conference, Port of Spain, Trinidad and Tobago, 25–26 June. The paper has not been peer reviewed. Asset evaluation embraces the integrated analysis of a hydrocarbon-bearing field, and the identification of suitable strategies for its future development, to add incremental value for the investor(s). Optimizing the evaluation process under uncertainty is important particularly in unconventional reservoirs, which hold large quantities of oil and gas resources but also exhibit large degrees of uncertainty. This paper describes a comprehensive optimization-under-uncertainty work flow that combines a simulation-based approach with semiautomatic work flows and high-speed computers to facilitate the process of decision-making for investors, using data from the Vaca Muerta Formation in Argentina as an example. Introduction An asset evaluation depends on many input parameters, some of which are partially known, partly analyzed, or un-available. Yet a go/no-go decision must be made, frequently within short time frames, because of competition, changing conditions, or the chance to take advantage of the business opportunity. The decision usually is based on preliminary assumptions and a conscientious analysis of several possible outcomes. Identifying the suitable future development strategies and the estimation of un-certainties in the input variables is crucial. Knowing the possible variability of the input and how the field mechanisms function will allow probabilistic forecasts of parameters such as production, costs, prices, and revenues. In the case of unconventional reservoirs with very limited history and high development costs, optimization under uncertainty plays a significant role in maximizing profit, reducing investment risk, and facilitating the decision-making process. The authors summarize the optimization-under-uncertainty work flow that was implemented for this study. The starting point is the development of a base-case, single-well, matched simulation model, and, where available, an extended model with history-matched offset wells. This is followed by sensitivity analysis to identify the most-influential parameters; uncertainty analysis and proxy modeling for developing probabilistic forecasting profiles (type wells); and optimization of key parameters under existing uncertainty, which is the final objective of this paper. The model and the uncertainty and optimization work flows have been built in the Petrel platform and all the simulations have been executed in the Eclipse compositional reservoir simulator, using published Vaca Muerta data. Base-Case Single-Gas-Well Model A critical element of any single-well simulation study is developing a base-case simulation model that correctly captures all the fluid-flow mechanisms that take place during the life of the well, while running as fast as possible. The characteristics of the base-case single-well simulation model and the mechanisms that were considered are discussed briefly, because the main objective of this work was to focus on probabilistic forecasting and optimization.

Acceleration of the NVT Flash Calculation for Multicomponent Mixtures Using Deep Neural Network Models

Phase equilibrium calculation, also known as flash calculation, has been extensively applied in petroleum engineering, not only as a standalone application for a separation process but also as an integral component of compositional reservoir simulation. Previous research devoted numerous efforts to improve the accuracy of phase equilibrium calculations, which place more importance on safety than speed. However, the equation-of-state-based flash calculation consumes an enormous amount of computational time in compositional simulation and thus becomes a bottleneck to the broad application of compositional simulators. Therefore, it is of vital importance to accelerate flash calculation without much compromise in accuracy and reliability, turning it into an active research topic in the past two decades. With the rapid development of computational techniques, machine learning brings another wave of technology innovation. As a subfield of machine learning, the deep neural network becomes a promising computation...

Reduced variables method for four-phase equilibrium calculations of hydrocarbon-water-CO2 mixtures at a low temperature

Carbon dioxide (CO2) flooding has been widely applied to enhance oil recovery. In low temperature reservoirs, CO2 injection may result in the formation of three hydrocarbon phases at thermodynamic equilibrium. In addition, an aqueous phase is always present in the reservoir. The aqueous phase can coexist with three hydrocarbon phases to form a four-phase equilibrium system. To evaluate CO2 utilization and storage in these low temperature petroleum reservoirs, a robust and efficient four-phase equilibrium calculation framework involving water is necessary for a compositional reservoir simulator. This is challenging not only because the number of variables increases but also because stability analysis becomes much complicated as the number of phases increases.In this research a novel four-phase equilibrium calculation framework is proposed for compositional reservoir simulation. A reduced variables method is used to solve four-phase flash problems efficiently and robustly. Multiphase flash calculations using reduced variables (RV) can converge to the equilibrium solution faster than formulations using conventional variables (Petitfrere and Nichita, 2015). Secondly, RV does not suffer from numerical problems in the Newton iterations when trace components are present in the aqueous phase. In addition to the implementation of the RV formulation, a systematic procedure consisting of stability analysis and flash calculations is proposed without any prior knowledge of initial K-values. Sets of different initial K-values are appropriately tested in each stability analysis.We performed comprehensive testing using characterized fluids found in the literature in order to validate the robustness of the proposed procedure. The four-phase regions in pressure-composition (Px) space could be accurately identified using our procedure. On the other hand, in some instances mixtures were incorrectly evaluated as three-phase when using existing approaches such as Li and Firoozabadi (2012). Our approach proposed in this paper is a promising four-phase equilibrium calculation framework for compositional reservoir simulation. The procedure achieved excellent robustness and efficiency with minimal modification to the conventional two or three-phase equilibrium calculation framework.

Geochemical Modeling of Wormhole Propagation in Carbonate Acidizing Considering Mineralogy Heterogeneity

SummaryMatrix acidizing is a widely used stimulation process during which acid is injected into the formation at pressures that are less than the fracturing pressure. The purpose of matrix acidizing is to improve formation permeability or to bypass damaged zones through rock dissolution. Because of precipitation and diagenesis, carbonate rocks might contain different compositions, including calcite (CaCO3), dolomite [CaMg(CO3)2], and other minerals. When acid is injected into the formation, reactions between acid and multiple minerals of carbonate rocks occur simultaneously. In this paper, a two–scale continuum model is implemented in UTCOMP, a 3D compositional reservoir simulator, coupled with IPhreeqc, a generalized code of PHREEQC as a module. PHREEQC is an open–source program that performs a wide variety of geochemical calculations (Charlton and Parkhurst 2011). For the UTCOMP/IPhreeqc coupled model, the two–scale continuum model describes the mechanisms of convection and dispersion, whereas IPhreeqc is used for the calculation of reactions between aqueous phase and minerals. We have validated the simulation model through comparison with the analytical solution. We also compared the UTCOMP/IPhreeqc coupled model with a simple–reaction model, in which the reaction is assumed to be a first–order reaction between acid and calcite, by modifying the IPhreeqc database to obtain consistent results. On this basis, we have investigated the effects of mineral compositions on acidizing efficiency and the wormhole–propagation process. It is found that mineralogy heterogeneity is a key factor that affects acidizing efficiency and the dissolution structure. This work contributes to simulating the acidizing process with complex geochemical reactions considering the chemistry of the aqueous solution interacting with minerals. The presented model improves our understanding of carbonate–acidizing optimization.

Application of proxy model to optimize base gas replacement by smart gas in underground gas storage process

One way to reduce the costs of investment in underground natural gas storage processes is to replace a portion of the base gas with a cheap gas. Optimizing the replacement amount of the base gas is one of the most attractive issues as mixing between the replaced base gas and the working gas and exceeding the injection pressure above the formation fracturing pressure are the two important phenomena that can occur and limit the amount of base gas replacement. In the present study, the concept of smart gas is applied to alleviate the encountered limitations during base gas replacement. Smart gas is referred to as the gas, the composition of which is adjusted so as to obtain the maximum amount of the base gas replacement. In the present work, dry air is used as the candidate gas to study the base gas replacement process in a depleted gas reservoir. Due to the fact that execution of a large-scale compositional reservoir simulator incorporated with an optimization algorithm is a time-consuming process, in order to calculate the optimum composition of the candidate gases, a proxy model is applied as a computationally inexpensive alternative to full numerical simulation. The results reveal that the composition of CO2 in air is an important parameter in controlling mixing between the candidate base gas and the working gas, while maintaining the injection pressure below the fracturing pressure of the formation. In addition, when the CO2 composition exceeds a specific value (40.95% in the modified air), the pressure that is required for gas production at a target rate cannot be supplied. The optimization of the CO2 composition in the candidate gas employing the proxy model shows that it is possible to replace 28.4% of the base gas and reach an enhanced gas recovery of about 18.55% in the reservoir under study using the optimized CO2 composition of 20.08% in the modified air as the replacing gas.

Effect of CO2-Water Injection Ratio on the Co-Optimization of Oil Recovery and CO2 Storage in a Swag Displacement Process

Carbon dioxide (CO2) injection has been studied and applied as an important Enhanced Oil Recovery (EOR) method. However, a low volumetric sweep efficiency has always been a technical issue for continuous CO2 flooding because of high mobility and low density of CO2 in comparison with those of other reservoir fluids. The low volumetric sweep leaves large volumes of bypassed oil in the reservoir which in turn leaves limited pore space available for CO2 storage. Therefore, several mobility control methods have been trialed in laboratory and field pilot tests to improve the sweep efficiency. One mobility control method is CO2 simultaneous water-and-gas (CO2-SWAG) injection. The injected water displaces the oil which is bypassed by the CO2 to enhance oil recovery. Recent laboratory studies have found that fraction of CO2 injected (FGI) in a CO2-SWAG process can affect CO2 relative permeability function [1], [2]. An optimized FGI reduces the CO2 relative permeability, hence increasing the amount of CO2 retained in the pore space. Although the previous studies have highlighted strong dependency of CO2 relative permeability on FGI, the number of experiments performed were limited to find the optimum value of FGI. In this study we performed laboratory experiments to find optimum FGI to maximize oil recovery and CO2 storage during CO2-SWAG displacement in a Bentheimer sandstone. A 28cm long Bentheimer core sample was used for the study. Before the SWAG injection, the core is at irreducible water saturation. The oil phase is composed of 65% Hexane (C6) and 35% Decane (C10). Experiments are run at 1700psia and 700C which represents near-miscible conditions. Pure Supercritical CO2 and distilled water are injected simultaneously into the core at a fixed FGI. A total of 8 experiments were performed at FGI of 0.0, 0.25, 0.50, 0.75, 0.80, 0.997 and 1.0. FGI of 0.0 and 1.0 represents water injection and continuous CO2 injection, respectively while a FGI of 0.997 represents CO2water saturated CO2 at the experimental conditions. The produced fluids are collected in glass vials and are subsequently analyzed using the Gas Chromatography to quantify the produced water and hydrocarbons. A gas flowmeter is used to measure the mass rate of gas. The volume of the produced liquid and differential pressure across the core are continuously recorded during the experiment. A compositional commercial reservoir simulator is used to determine the FGI dependent relative permeability functions. Pressure drop across the core, oil recovery and the mass of CO2 stored are used as the matching parameters. The results indicate that 0.80 is the optimum FGI for the given experimental conditions. A remarkable reduction in CO2 relative permeability was observed for FGI 0.75 compared with continuous CO2 injection (FGI=1).

Mechanistic modeling of carbonated waterflooding

CO2 enhanced oil recovery is usually affected by poor sweep efficiency due to unfavorable mobility contrast between the injected CO2 and oil. To alleviate this problem, CO2 is added to the injected brine and transported in the reservoir by flood water. Therefore, Carbonated Water Injection (CWI) takes advantage of both CO2 and water flooding processes. Furthermore, geochemical reactions between the injected carbonated brine and rock can alter petrophysical properties of the reservoir and affect final oil recovery. While there are several CWI coreflood experiments reported in the literature, simulation studies for this process are scarce.Accurate modeling of CWI performance requires a simulator with the ability to capture true physics of the CWI process. In this study, a compositional reservoir simulator developed at The University of Texas at Austin, UTCOMP, coupled with a state-of-the-art geochemical package developed by United States Geological Survey, IPhreeqc, is used to model CWI process. We considered the impact of CO2 mass transfer between brine and hydrocarbon phases based on thermodynamic constrains at the reservoir condition. In order to validate our simulation approach, the results of our CWI simulations were compared with a recently published coreflood experiment. Moreover, we investigated the fluid-rock interactions in CWI.The results of the simulations, indicated that prior to water breakthrough the main drive mechanism is displacement. But as more carbonated water is injected, CO2 diffuses more into the trapped oil left behind, which results in oil swelling and subsequent oil viscosity reduction. Moreover, reaction of carbonate minerals such as calcite with carbonated brine results in dissolution of the main rock matrix which consequently creates wormholes similar to carbonates acidizing.In this study we propose a novel approach for accurate modeling of carbonated waterflooding process. The results of this study highlight the importance of geochemical reactions in modeling CWI process. Our approach has been validated based on history matching at the backdrop of a recently published coreflood experiment.

Simultaneous effect of geological heterogeneity and condensate blockage on well test response of gas condensate reservoirs

Condensate dropout around a wellbore causes many practical difficulties. It complicates the prediction of fluid flow around a wellbore, especially during well test interpretations. Many researchers have explored the impact of gas condensing on the transient behavior of well-test responses. However, much less attention were paid to the analysis of the combined effect of geological heterogeneity and condensate drop out. In this study, a geostatistical approach (ordinary Kriging) is used to build reservoir models with different amount of heterogeneity. These models are simulated by a compositional reservoir simulator to generate pseudo-pressure data versus time of these heterogeneous models. The common method of the single-phase gas pseudo-pressure approach is used to evaluate the static and dynamic parameters of these reservoir models during well test interpretations. It is revealed that the intensity of the condensate formation around the wellbore is a strong function of the reservoir heterogeneity. The results show that increasing of the model heterogeneity can entirely complicate the well-test responses when condensate drops near the wellbore region. Implementing two well tests (one above and another below the dew point pressure) can enable us to distinguish the geological heterogeneity from the liquid condensation effect when both of them coexist in the formation.

An Evaluation of Steady-State-Based Pressure-Saturation Predictions in Gas-Condensate Reservoirs Under Various Reservoir and Well Operating Conditions

Summary The analysis of pressure-transient and rate-transient data in gas-condensate reservoirs (GCRs), operating below dewpoint pressure (Pdew) often requires using two-phase pseudovariables. These are used to handle the nonlinearities that arise in the governing partial-differential equation as a result of pressure-dependent saturation, mobility, and fluid-property changes. The incorporation of relative permeability (kr) data in the computation of such pseudovariables requires a priori knowledge of sufficiently representative pressure-saturation relationships. For this purpose, a number of analytical and semianalytical pressure-saturation prediction methods can be found in the literature. In this study, a pressure-saturation prediction method, which relates component weight fractions (in the gas phase, the condensate phase, and the reservoir fluid mixture) and gas fractional flow [or gas to total gas-plus-condensate flow rate (GTR)] to pressure, was used to compute pressure-saturation relationships for various reservoir fluids and two rock types. The computed pressure-saturation relationships were compared to the responses observed in the near-wellbore region of corresponding single-well 1D radial homogeneous compositional reservoir-simulation models operated at constant well flowing pressures (Pwf) lower than Pdew. Sensitivities were conducted to highlight the impact of gas-condensate fluid richness, kr characteristics, degree of reservoir undersaturation (Pi – Pdew), and Pwf on observed trends in the representativity of the predicted pressure-saturation relationships. Like other methods based on two-phase steady-state (SS) assumptions, the condensate saturations predicted by the GTR method generally matched those observed around the wellbore in the simulation models when Pi – Pdew was sufficiently high. For cases in which Pi – Pdew was not sufficiently high, an examination of radial pressure and saturation profiles from the simulation models showed that the trend of increased condensate saturation levels observed around the wellbore with increasing degrees of reservoir undersaturation, as reported in literature, represents one of three possible trends (i.e., increasing, decreasing, and almost-constant condensate-saturation levels). For cases in which the predicted GTR-based pressure-saturation curve showed lower (higher) condensate saturations than the pressure/volume/temperature (PVT) liquid-saturation curves at the given Pwf, increasing Pi – Pdew resulted in decreasing (increasing) condensate-saturation levels around the wellbore. The results indicate that at low Pi – Pdew values, trends in two-phase SS-based analysis techniques are affected by the gas-condensate fluid richness and the nature of the kr characteristics. It is also shown that for cases in which there is not much of a difference between PVT-based and SS-based pressure-saturation curves, variations in the Pi – Pdew do not affect the condensate-saturation levels around the wellbore.

Lean gas Huff and Puff process for Eagle Ford Shale: Methane adsorption and gas trapping effects on EOR

In order to improve the current recovery factor of unconventional reservoirs and take it to the next level (10–20%), gas Huff and Puff is a very promising enhanced oil recovery technology used currently. Many laboratory works and field pilot tests have been conducted recently. However, most of the research work is based on CO2 as an injected gas. Few of them were built upon the more realistic lean gas Huff and Puff process. From the feedback of lean gas Huff and Puff pilot in Eagle Ford, the field response is always beyond analysis and reservoir simulation forecast. Methane, the main component of lean gas, and its adsorption and desorption effects on the unconventional resources are pronounced due to nanopore confinement effects and a much larger surface area than conventional reservoirs. On the other hand, the minimum miscibility pressure of lean gas in Eagle Ford is high (>4000 psi), which makes gas phase and liquid phase still separate in a long period of Huff and Puff process before reservoir pressure is elevated high enough to developing the supercritical state. Therefore, relative permeability hysteresis – gas trapping effect in nature, should be also very significant on gas EOR performance for unconventional resources. To our best knowledge, there are no such studies about the lean gas adsorption and gas trapping for Huff and Puff process of unconventional reservoir reported so far. In this study, these two mechanisms are systematically analyzed and quantified in a compositional reservoir simulation study. The gas EOR dependence on permeability is also studied and the resulting gas injectivity problem is answered, trying to explore the real problem encountered in the pilot practice.

Embedded discrete fracture modeling for compositional reservoir simulation using corner-point grids

Abstract Corner-point is an industry-standard type grid for application in reservoir simulation. The flexible gridding in corner-point grids provides several advantages over Cartesian grids for the representation of complex geological features. In this work, an embedded discrete fracture model (EDFM) is extended to corner-point grids with a full-permeability-tensor formulation to simulate complex fractures in this type of grids. The developed model is implemented in an IMPEC, compositional reservoir simulator. We first describe the formulations of the full-permeability-tensor implementation together with the modified governing equations for EDFM simulations. Then, we present methodologies for computing matrix-fracture intersections and fracture-fracture connections in the EDFM considering the various block geometries in corner-point grids. Subsequently, case studies are presented to verify the developed model, where the impacts of grid distortion and cross derivatives on simulation results are discussed. Three-dimensional case studies are also shown to illustrate the influence of natural fractures on secondary recovery. The results of this study demonstrate the reliability of the developed model, and they also show the compatibility of the EDFM with different types of numerical solution schemes in existing simulators.

Upscaling of CO2 injection in a fractured oil reservoir

The purpose of this study is to present the numerical evaluation of the upscaling of secondary and tertiary CO2 flooding at reservoir pressure and temperature. In all simulation cases, CO2 is injected from the top of the fracture in a fracture-matrix block system and displaces oil toward the bottom of the block. Our numerical models provide comprehensive sensitivity analyses that are proved to be important for the upscaling of CO2 injection from lab to reservoir scale. In addition, we address the scale dependency of the diffusion mechanism in a large matrix-fracture domain.In our earlier studies, we have conducted a significant number of reservoir-condition CO2 flood experiments by employing fractured core systems and North Sea reservoir oil. Our experimental and numerical studies are proved to be useful to investigate the complex physical phenomena affecting the efficiency of CO2 injection in oil recovery from a fracture reservoir. The height of the core plugs used in our experiments varies from 7 to 28 cm while the fracture spacing varies between ∼4 cm and 12 cm. In all cases, we find the important role of diffusion mechanism in increasing oil recovery in a fracture-matrix system at the lab scale. However, the field matrix block size is often 1-to-2 orders of magnitude larger than that in the lab scale. Recovery mechanisms are significantly affected by the matrix dimension. For instance, the impact of diffusion mechanism on oil recovery may alter with the size of the matrix block and the fracture spacing. This highlights the importance of upscaling from lab to the field scale that is addressed in this study.We employ a compositional reservoir simulation with a developed equation of state (EOS) to model CO2 injection in a fracture-matrix system at reservoir conditions. In all the numerical simulations, the single-porosity (SP) model consists of a single axial fracture with a typical fracture aperture of 1 mm. The SP model also contains a single matrix block that feeds the producer through fracture next to it. The dimension of the fracture-matrix block varies in the SP models to account for the scale dependency of the results. In our modeling framework, we assume a network of open fractures that are homogenously distributed in the reservoir and links the oil in the matrix to the production wells. We initialize the matrix with the North-Sea-Chalk-Field (NSCF) live oil and the fracture with the injected gas (CO2). The whole displacement process is operated at the constant reservoir conditions of 3750 psia and 110 °C, representing typical NSCF reservoir conditions. Prior to conducting simulations, we successfully tune EOS parameters against the extensive PVT data, sampled from a specific fractured NSCF reservoir. The tuned EOS model assures the proper estimation of the complex phase and volumetric properties for CO2 and oil mixtures at reservoir conditions. In addition, the multi-component diffusion coefficients that are tuned using experimental data are employed as input parameters for the compositional simulations.We study the effect of several key parameters on oil recovery; e.g. matrix block size, fracture spacing, CO2 injection rate, density difference, vaporization and the diffusion. The results show that the mass transport is mainly dominated by diffusion in the lab scale which is not the case for the large matrix block size. Finally, we test the accuracy of the dual-porosity (DP) model against the SP model at various displacement conditions. Results show that when diffusion and vaporization are significant the DP model fails to predict the results of the SP reservoir model.Our findings are an important step towards modeling the tertiary-CO2 flooding in an actual fracture-chalk system. We also provide some important inputs that are necessary for upscaling tertiary-CF from a lab-scale into a field-scale reservoir model.

CO2 adsorption the numerical simulations and the controlling factors to low rank coal

Carbon Capture Sequestration (CCS) in unmineable coal seams questionably gives benefits for the commercial success through potential release of additional methane during the injection of CO2 adsorbs into the coal seams, the process known as enhanced coalbed methane (ECBM) recovery. However, a significant concern lies in the loss of injectivity due to reduction in permeability by coal matrix swelling occurences with CO2 adsorption although this effect can be partially be offset with ‘huff and puff’ scheme of cyclic CO2 injection followed by extraction of the released methane. The paper discusses the results of a numerical simulation study carried out with GEM compositional reservoir simulator to evaluate the effects of uncertainties in various reservoir parameters on the overall volume of CO2 storage and additional methane recovery of low rank coalfield. A 12-15m thick seam at shallow depth, 50-75 m was considered for fluid flow simulation study. While some information on the reservoir setting was obtained through literature and personal communication with the CBM operators, the rest of the information was derived through laboratory studies. The reservoir parameters considered for the study are injection pressure, adsorption capacity, cleat permeability and porosity, and initial gas saturation. A 100-acre drainage area with 5-spot vertical well pattern was considered with one central injector and four producers on four corners of the study area. The maximum allowable injection pressure was estimated to be 7500 kPa at the reservoir setting. The injection pressure was varied from 1000 kPa to 7500 kPa in the simulation. A number of adsorption isotherms were established in the laboratory. The variations in the adsorption parameters observed through the isotherms were considered as uncertainty in the storage capacities. Significant variations were observed due to the variation in adsorption isotherms both for CO2 storage and additional methane recovery. Fracture permeability was varied from 3 md to 200 md, which is the range of permeability observed in the coalfield is around 100-200 mD. The results of simulation indicate a strong influence of porosity on the CO2 storage and ECBM recovery. Fluid flow simulation study shows that variation in sorption time has no significant effect for a low permeability situation while some marginal effect in high permeability situation. Cleat porosity was varied from 1 % to 10 %. Within this range of porosities, enhanced methane recovery varied from 1 % to 10 % relative to the primary recovery but the volume of stored CO2 did not vary significantly. Lastly, the pore pressure, adsorption and gas saturation of CO2 sequestered volume and additional methane recovery were found to increase substantially.

Mechanistic simulation study of gas Puff and Huff process for Bakken tight oil fractured reservoir

Current US unconventional resource revolution has more than doubled US oil production and made US the largest oil production country in the world recently. Although the industry is trying all means including screening sweet spots, conducting infill drillings and optimizing the well spacing to improve recovery factor (RF) from primary production, unconventional reservoirs will encounter what the past conventional reservoir production experienced: difficult to explore new giant resource, run out of ‘easy oil’ (sweet spot), production comes to a plateau and declines. Therefore, it is time to implement enhanced oil recovery for unconventional resources. The goal of this work is to improve oil recovery by implementing and optimizing gas Huff and Puff method for one of the US major tight oil reservoirs-Bakken formation. Pressure dependent permeability and porosity are implemented into the compositional reservoir simulation to represent geomechanics effects. Gas diffusion effects is systematically analyzed to quantify the importance comparing with the natural fracture system in tight oil fractured reservoirs. Alternative gas composition instead of using CO2 in the Huff and Puff process is investigated and the recovered oil components is analyzed. The complex natural fracture structure and natural fracture permeability proved to be important affecting factors to oil production of Bakken reservoirs.

EOS lumping optimization using a genetic algorithm and a tabu search

To reduce CPU time in compositional petroleum simulation models (e.g. compositional reservoir simulations), a minimum number of components should be used in the equation of state (EOS) to describe the fluid phase and volumetric behavior. A ″detailed” EOS model often contains from 20 to 40 components, with the first 10 components representing pure compounds and the remaining components represent a split of the heavier C6+ material in single-carbon-number (SCN) fractions. A ″pseudoized” (or lumped) EOS model might contain only 6–9 lumped components. The selection of which components to lump together is difficult because of the huge number of possible combinations.This paper describes an automated method to find the best pseudoized EOS model based on an initial detailed SCN EOS model. The method is based on (1) a fitness function quantifying the quality of match between a pseudoized EOS model and the detailed SCN EOS model from which it is derived, (2) a genetic algorithm used to obtain a first initial solution and (3) a tabu search to refine this initial solution and find the optimal lumping scheme. The method allows for a set of constraints to be imposed on the lumping of components, such as (1) not lumping certain components (e.g. CO2), (2) forcing lumping of some components (e.g. i−C4 and n−C4).The proposed procedure was successfully able to find the optimal lumped EOS from a detailed SCN EOS with 34 components for three scenarios with different number of components in the lumped EOS (15, 9 and 6). The runtime is ranging from 10 to 45 min.

Enhanced oil recovery efficiency of low-salinity water flooding in oil reservoirs including Fe2+ ions

The low-salinity waterflooding is an attractive eco-friendly producing method, recently, for carbonate reservoirs. When ferrous ion is present in the formation water, that is, acidic water, the injection of low-salinity water generally with neutral pH can yield precipitation or dissolution of Fe-minerals by pH mixing effect. FeSO4 and pyrite can be precipitated and re-dissolved, or vice versa, while siderite and Fe(OH)2 are insoluble which are precipitated, causing permeability reduction. Particularly, pyrite chemically reacts with low-salinity water and release sulfate ion, altering the wettability, favorably, to water-wet. In this aspect, we analyzed oil production focusing on dissolution of Fe-minerals and Fe-precipitation using a commercial compositional reservoir simulator. From the simulation results, the quantities of precipitation and dissolution were enormously large regardless of the type of Fe-minerals and there was almost no difference in terms of total volume in this system. However, among Fe-minerals, Fe(OH)2 precipitation and pyrite dissolution were noticeably large compared to troilite, FeSO4, and siderite. Therefore, it is essential to analyze precipitation or dissolution for each Fe-mineral, individually. Meanwhile, in dissolving process of pyrite, sulfate ions were released differently depending on the content of pyrite. Here, the magnitude of the generated sulfate ion was limited at certain level of pyrite content. Thus, it is necessary to pay attention for determining the concentration of sulfate ion in designing the composition of injection water. Ultimately, in the investigation of the efficiency of oil production, it was found that the oil production was enhanced due to an additional sulfate ion generated from FeS2 dissolution.

Three-Phase Relative Permeability and Capillary Pressure Models With Hysteresis and Compositional Consistency

SummaryNew coupled three-phase hysteretic relative permeability and capillary pressure models have been developed and tested for use in compositional reservoir simulators. The new formulation incorporates hysteresis and compositional consistency for both capillary pressure and relative permeability. This approach is completely unaffected by phase flipping and misidentification, which commonly occur in compositional simulations. Instead of using phase labels (gas/oil/solvent/aqueous) to define hysteretic relative permeability and capillary pressure parameters, the parameters are continuously interpolated between reference values using the Gibbs free energy (GFE) of each phase at each timestep. Models that are independent of phase labels have many advantages in terms of both numerical stability and physical consistency. The models integrate and unify relevant physical parameters, including hysteresis and trapping number, into one rigorous algorithm with a minimum number of parameters for application in numerical reservoir simulators. The robustness of the new models is demonstrated with simulations of the miscible water-alternating-gas (WAG) process and solvent stimulation to remove condensate and/or water blocks in both conventional and unconventional formations.

Numerical Modeling and Validation of a Novel 2D Compositional Flooding Simulator Using a Second-Order TVD Scheme

The aim of this paper is to present the latter and develop a numerical simulator aimed at solving a 2D domain porous medium, using the compositional approach to simulate chemical flooding processes. The simulator consists in a two-phase, multicomponent system solved by the IMplicit in Pressure, Explicit in Concentration (IMPEC) approach, which can be operated under an iterative/non-iterative condition on each time-step. The discretization of the differential equations is done using a fully second order of accuracy, along with a Total Variation Diminishing (TVD) scheme with a flux limiter function. This allowed reducing the artificial diffusion and dispersion on the transport equation, improving the chemical species front tracking, decreasing the numerical influence on the recovery results. The new model was validated against both commercial and academic simulators and moreover, the robustness and stability were also tested, showing that the iterative IMPEC is fully stable, behaving as an implicit numerical scheme. The non-iterative IMPEC is conditionally stable, with a critical time-step above which numerical spurious oscillations begin to appear until the system numerically crashes. The results showed a good correspondence in different grid sizes, being largely affected by the time-step, with caused a decrease in the recovery efficiency in the iterative scheme, and the occurrence of numerical oscillations in the non-iterative one. Numerically speaking, the second-order scheme using a flux splitting TVD discretization proved to be a good approach for compositional reservoir simulation, decreasing the influence of numerical truncation errors on the results when compared to traditional, first-order linear schemes. Along with these studies, secondary recoveries in constant and random permeability fields are simulated before employing them in tertiary recovery processes.