Numerical Reservoir Simulation Research Articles

Modeling of coupled wellbore-reservoir flow in steam-like supercritical geothermal systems

The capabilities to efficiently model the transport of mass and heat in geothermal reservoirs at supercritical conditions are necessary to support the efforts to explore and utilize supercritical hydrothermal systems, as well as to extend the utilization of existing geothermal fields at greater depths, where supercritical temperatures might be encountered. Similar capabilities are needed to model the flow in wells producing supercritical fluids, possibly in a coupled way with the reservoir flow. As a step in this direction, the new EOS2H module for sub-critical and steam-like super-critical H2O-CO2 mixtures has been linked to T2Well, the extension of the TOUGH2 numerical reservoir simulator developed for the transient modeling of fully coupled wellbore-reservoir flow. The resulting code is verified against simulations performed with other supercritical reservoir simulators and steady-state subcritical and supercritical wellbore flow simulations. It is then used to perform coupled wellbore-reservoir flow simulations at the well-sector scale relevant to the conditions found in well IDDP-1, Krafla, Iceland.

Capacitance-Resistance Model Used for Integrated Detection of Water Production

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19333, “Integrated Detection of Water Production in a Highly Heterogeneous and Tight Formation Using a CRM Model: A Case Study on Waterflooding Gaither Draw Unit, Wyoming, USA,” by Kailei Liu, China University of Geosciences; Xingru Wu, SPE, University of Oklahoma; and Kegang Ling, University of North Dakota, prepared for the 2019 International Petroleum Technology Conference, Beijing, 26–28 March. The paper has not been peer reviewed. Copyright 2019 International Petroleum Technology Conference. Reproduced by permission. The Gaither Draw unit is a heterogeneous tight formation with an average permeability of less than 0.1 md. After substantial water injection, there was no clear benefit of injected water for any producer. However, knowing the distribution of the injected water is critical for future well planning and quantification of injection efficiency. The objective of this study is to show how the capacitance-resistance model (CRM) was used on this field and how it validated the use of other independent methods. This paper demonstrates that integration of different sources of data in reservoir management is critical. Introduction Unlike numerical reservoir simulation, the CRM requires only the injection rates of each injector and the production rates of each producer as input to evaluate reservoir performance. The connectivity and the time constant that are estimated by fitting production rates can provide useful information about geological features and reservoir heterogeneity. With a clear understanding of reservoir heterogeneity, flow barriers and high-permeability zones can be identified. Significant reservoir heterogeneity may lead to poor sweep efficiency. These characteristic features make the CRM (the development of which is outlined in the complete paper) a unique and practical tool to investigate water-flooding projects. Basics of the CRM The root of the CRM is the governing material-balance equation stating that the mass difference in an arbitrary control volume (CV) is equal to the mass difference between injected and produced fluid passing through this CV. Then, the governing material-balance equation is combined with a linear productivity model to obtain an ordinary differential equation. The semianalytical solution of the ordinary differential equation forms the CRM. In the field case, there are often many injectors and producers, which would be a large nonlinear optimization problem in the CRM. A CRM-producer (CRMP) model can be used to reduce the number of time constants and increase computational speed. The equations contributing to such a model are provided in the complete paper. Case Study The study area is located in the Parkman reservoir, which is a part of the Powder River Basin in southeast Montana and northeast Wyoming. The Parkman Sandstone Member of the Upper Creta-ceous Mesaverde formation is the oldest sandstone member in a widespread cycle of late Cretaceous regression. The Parkman reservoir consists of multiple stacked sands 5,000 to 9,500 ft deep. There are three dominant lithologies of the Parkman reservoir. The first is pro-delta shale and siltstone, which includes very fine and well-sorted sandstone. The second is medium-grained sandstone, which lies as coarsening-upward successions mixed with siltstone. The last is silt and mudstone, which gradually changes from exhibiting carbonaceous to lignitic characteristics.

An oil–water two-phase reservoir numerical simulation coupled with dynamic capillary force based on the full-implicit method

Capillary pressure is a key parameter in reservoir numerical simulation. It is generally considered that the capillary force is an only function of water saturation in the current traditional reservoir numerical simulation methods. However, large amount of indoor experiments have shown that capillary force is not only dependent on the water saturation, but also the velocity of fluid flow. In this work, we introduce the dynamic capillary pressure to the oil–water two-phase flow equations. Then, we discrete the modified oil and water phase basic governing equation with difference methods. The full implicit method is selected to solve the pressure equation. Finally, a numerical simulation method coupled with the dynamic capillary force based on the full-implicit method is established. We discussed the effect of dynamic capillary pressure on the oil–water two-phase flow in two-dimensional homogeneous and heterogeneous porous media. Comparing to static capillary pressure model, the dynamic capillary pressure model predicts higher water-saturation near the injection and production wells. In addition, a “funnel” like pressure profile appears in the water saturation profile. At low water cut stage, the rate of water cut increase is relatively high, while opposite is true at high water cut stage. We also calibrated our newly proposed model by comparing to the analytical method and the actual oil field production data. Our work should provide important insights into the effect of dynamic capillary force on the oil–water two-phase flow and better estimation of flow behavior in oil and gas reservoir.

Estimation of H2S Produced from Reservoir Souring

The increase of the produce H2S due to water injection is known as reservoir souring. The sulfate reduced bacteria (SRB) which may be exist in the injected water reduces the sulfate which already existing in the reservoir. This study includes prediction of H2S for Mauddud reservoir in the Ahdeb oilfield by using specialized reservoir numerical simulator. Reservoir souring modeling utilized to enable operations to make better decisions for remedial actions to either prevent souring or to mitigate its impact. The aim of this study is to estimate the probability and timing of the start of H2S production in produced fluids. The results showed that the maximum concentration of H2S in the prediction production well was reached to 2.9 Ibm/day which occurs after 180 days this carry out when the SRB concentration was about 2000 ppm .The SRB concentration is increasing in areas where the sulfate is in high concentration and also there is a direct relationship between the SRB concentration and the H2S concentration

Development of hybrid low salinity water polymer flooding numerical reservoir simulator and smart proxy model for chemical enhanced oil recovery (CEOR)

A MATLAB based finite volume CEOR simulator was developed for modeling the recovery of oil by low salinity water polymer flooding (LSWPF) in a proposed geostatistical method of generating porous media. Firstly, this paper coupled ion exchange, wettability and fluid flow transport models to the proposed heterogeneous porous media to investigate LSWPF oil recovery performance. Secondly, for the first time, a new hybrid smart proxy model of particle swarm optimization artificial neural network (PSO-ANN) recovery factor (RF) correlation was derived for LSWPF oil recovery performance using the extracted weights and biases. The proposed porous media as well as stochastic multi-fractal random field and circulant embedding method (CEM) permeability field realizations did not exhibit artificial banding unlike turning bands method (TBM) realizations when compared. A fast convergence pre-conditioner solution technique of algebraic multigrid (AMG) linear solver combined with an acceleration method of biconjugate gradients stabilized method (BI-CGSTAB) was used to solve the fully implicit system of coupled linearized equations. The simulator was validated with PHREEQC geochemical package and a secondary LSWPF coreflood test with outstanding history match. Numerical 2D examples study in this paper revealed the synergetic effects of LSWPF and high salinity water polymer flooding (HSWPF) with higher oil RFs of 39% and 31%, respectively than the standalone LSWF of 23% and normal waterflood of 11% for 50 days of production. The optimal architecture of the PSO-ANN RF model is 7 inputs, 3 hidden neurons and 1 output. Also, the PSO-ANN RF model predictions were compared to the LSWPF numerical simulation results with the former remarkably having reduced computational time in seconds with training and blind testing errors <1% average absolute percentage error (AAPE) for various permeability field realizations investigated. Finally, the PSO-ANN RF model will serve as a fast-alternative reservoir management and planning tool to supplement the high-fidelity LSWPF CEOR simulator developed to minimize computational overheads, risks and uncertainties in oil recovery performance modeling.

A review of proxy modeling applications in numerical reservoir simulation

The applications of numerical simulation modeling such as assisted history matching, production optimization, and reservoir performance forecasting usually lead to significant time, computer resources, and accordingly huge associated costs. To overcome these circumstances, proxy modeling is the suggested approach to deliver a cheap alternative and approximate nonlinear oil and gas reservoir responses such as recovery factor and reservoir operating parameters and characteristics. Proxy modeling is considered a powerful tool for optimization and uncertainty analysis alike. Conventionally, the reservoir simulation is solved by tuning selected uncertain reservoir parameters one at a time. While, the most important feature of proxy modeling is the ability to determine the combined effect of uncertain parameters at the same time. Petroleum engineering field usually requires a wide variety of parameter estimation problems, and proxy modeling can satisfy this demand. Proxy modeling can be developed based on different methods; the current paper presents a comprehensive review of the methods of developing of proxy modeling and their applications in reservoir simulation since proxy modeling is becoming more widely used as a simplify highly complex substitution to reservoir simulation with reasonable accuracy. This article reviews the methods used in recent years to develop proxy models as a successful replica of complex numerical flow simulation models. This will shed the light of different proxy models, their uses, and how to judge on their efficiencies.

A comparative study between finite difference explicit and implicit method for predicting pressure distribution in a petroleum reservoir

Predicting pressure distribution in a petroleum reservoir is principal to the reservoir’s evaluation and maintenance, as pressure changes with space and time. A convenient approach to effectively achieve this task is to formulate fluid flow equations based on the reservoir characteristics and solve them numerically. Numerical method provides solutions to mathematical fluid flow models developed in a reservoir simulation. This study provides numerical solutions, using both finite difference explicit and implicit method, to a mathematical model by developing MATLAB codes to ascertain the pressure distribution for a singlephase, one-dimensional, slightly compressible fluid flow in a petroleum reservoir. Series of numerical simulations were carried out during the first year of production using timestep sizes of 1, 2 and 3 days, respectively. The explicit method gave poor result (negative values for pressures) for timestep of 1 day, an outcome that is not accurate to describe the problem being solved but gave acceptable pressure results for timestep of 2 and 3 days. This shows that the efficacy of the explicit method is reliant on the chosen timestep and simulation time. In contrast, the implicit method gave a quite satisfactory results for all timesteps, and including less than 1 day, confirming the robustness and unconditionally stable nature of the implicit method. A commercial simulator (CMG software) was also employed to build a one-dimensional black oil model to validate the aforesaid results of which a close match was observed between the simulator results and the numerical solutions. This study provides insights to reservoir’s pressure profile during hydrocarbon recovery beforehand so that efficient pressure maintenance decisions can be made to achieve economic hydrocarbon recovery throughout the life of the reservoir. Keywords: finite difference formulation; explicit and implicit method; petroleum reservoir; pressure distribution; numerical reservoir simulation

A fuzzy logic approach for estimating recovery factors of miscible CO2-EOR projects in the United States

The recovery factor (RF) is one of the most fundamental parameters that define engineering and economical success of any operational phase in oil and gas production. Despite its importance, it is not easy to predict recovery factors as they are affected by many variables including the type of the recovery process, reservoir type, fluid properties, reservoir heterogeneity, depth, and thickness. The usual and most comprehensive method of estimating recovery factors is laboratory experiments or numerical reservoir simulation, or combination of both, each of which has their own limitations due to data requirements, boundary conditions and scale effects.In this work, a fuzzy inference system approach has been adopted to predict miscible CO2-EOR recovery factors of the major field applications in the United States using a more practical, multi-variable, inexpensive, quicker, and yet accurate alternative to conventional approaches. The fuzzy system was built using Mamdani-type fuzzy logic inference engine. Reservoir data compiled from different sources were used as model input parameters (9 of them) and associated recovery factors obtained from literature survey were used as control variable, i.e. output. Due to the limited number of field cases that could be used for this purpose, 24 sets of applications were included in the study. The model showed reasonable predictive capability for the field observations of recovery factor despite the complexity and dependence of this parameter on different inputs. In addition, the multidimensional fuzzy solution was used to demonstrate response of miscible CO2-EOR recovery factor to different inputs. The fuzzy system introduced in the paper can be used to predict RFs and also as a guidance tool for making decisions based on the sensitivity to different inputs.

Linear and radial flow modelling of a waterflooded, stratified, non-communicating reservoir developed with downhole, flow control completions

Forecasts of the performance of waterflooded oil-field have been prepared for many years using fractional flow models, such as those by (Buckley-Leverett, 1942) (BL), (Welge, 1952) and (Dykstra-Parsons, 1950) (DP), to estimate the vertical sweep efficiency between wells. These methods, and their later modifications, formed the theoretical basis for designing a water-flooded oil-field. Advanced Well Completions (AWC) incorporating downhole flow control device (FCD) technology have become a proven method for modifying a production well's inflow profile, delaying water breakthrough, improving sweep efficiency and enhancing recovery.This manuscript extends the fractional flow model describing the performance of a waterflooded, stratified-reservoir with an AWC installed in the production well. The piston-like behaviour of the water-front described by previous, semi-analytical models is extended here to the linear and radial flow modelling of more realistic displacement profiles.This paper describes the theoretical basis and application workflow along with several case studies illustrating their performance and value. The accuracy of the new, semi-analytical models are verified by comparison against the results from a numerical reservoir simulator. Model limitations and possible future extension are also discussed.The workflow can be implemented as either a production forecast or a diagnostic tool for an AWC well in a waterflooded oil-field. It provides one missing link between today's AWC design workflows and the long-term value evaluation of a specific AWC design when a commercial numerical simulation software is either not available or too time consuming.

Study on reasonable gas recovery rate and adjustment scheme of bottom water gas reservoir

During the exploitation of bottom water gas reservoirs, the gas wells have a serious impact on the development of gas reservoirs. Based on the systematic analysis of the production dynamics and water intrusion characteristics of the bottom water gas reservoir in the XS gas field, combined with seismic, geological, well logging, production testing and other data, a numerical simulation model was established to demonstrate the rational gas production rate after gas reservoir seeing water. Research on the mechanism of water control. The gas reservoir numerical simulation technology is used to determine the reasonable gas production speed of the gas well, which can alleviate the bottom water coning rate and prolong the waterless gas production period. Combined with measures such as drainage, water shutoff, and shut-in, the rational gas production rate and adjustment measures for the bottom water gas reservoir are formulated.

Proper Orthogonal Decomposition-Based Method for Predicting Flow and Heat Transfer of Oil and Water in Reservoir.

Calculation process of some reservoir engineering problems involves several passes of full-order numerical reservoir simulations, and this makes it a time-consuming process. In this study, a fast method based on proper orthogonal decomposition (POD) was developed to predict flow and heat transfer of oil and water in a reservoir. The reduced order model for flow and heat transfer of oil and water in the hot water-drive reservoir was generated. Then, POD was used to extract a reduced set of POD basis functions from a series of "snapshots" obtained by a finite difference method (FDM), and these POD basis functions most efficiently represent the dynamic characteristics of the original physical system. After injection and production parameters are changed constantly, the POD basis functions combined with the reduced order model were used to predict the new physical fields. The POD-based method was approved on a two-dimensional hot water-drive reservoir model. For the example of this paper, compared with FDM, the prediction error of water saturation and temperature fields were less than 1.3% and 1.5%, respectively; what is more, it was quite fast, where the increase in calculation speed was more than 70 times.

Application of Machine Learning and Artificial Intelligence in Proxy Modeling for Fluid Flow in Porous Media

Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.

A MINLP model for the optimal waterflooding strategy and operation control of surface waterflooding pipeline network considering reservoir characteristics

Waterflooding is one of the most effective methods for oilfield secondary recovery. For the optimal waterflooding strategy and surface waterflooding pipeline network operation control plan, this paper proposes a mixed integer nonlinear programming (MINLP) model with the total profit of waterflooding development as the objection function, considering the oil reservoir characteristics. Based on the reservoir numerical simulation, the relationships between the water injection and oil production of four reservoir types are acquired. In the proposed model, various technical and operational constraints are considered for the safe and efficient operation of the pipeline network. Due to the nonlinearity of partial equations in the model, a piecewise linear approximation method is adopted in this paper, and the proposed MINLP model is converted into a mixed integer linear programming (MILP) model, which can be solved by the commercial solver. Finally, taking a real-world waterflooding oilfield in Daqing, China as an example, the applicability of the model are testified, and the superiority of considering the reservoir characteristics is detailedly illustrated.

GPU Parallelization Nested Decomposition Method for Solving Large Linear Systems in Reservoir Numerical Simulation

This paper designs a highly parallel Nested Factorization (NF) to solve large linear equations generated in reservoir numerical simulation problems. The NF method is a traditional linear solution preprocessing method for reservoir numerical simulation problems, and has regained attention in recent years due to its potential to extend to parallel architectures such as GPUs (Graphics Processor Units). The parallel algorithm of this paper is based on the MPNF (Massively Parallel Nested Factorization) framework proposed by Appleya (Appleyard, Appleyard, Wakefield, &amp; Desitter, 2011). The MPNF algorithm designed in this paper focuses on its efficient implementation on the GPU parallel architecture. Its features include: using a custom matrix structure to achieve merge access, improving access bottlenecks and improving the efficiency of the SpMV algorithm. It is also applicable to the two-stage preprocessing method CPR. (Constrain Pressure Residual) pressure solution and global preprocessing stage; the MPNF method is extended to the solution of 2.5-dimensional unstructured grid problem. The parallel algorithm in this paper has been integrated into the reservoir numerical simulator. For the SPE10 (million grid, highly heterogeneous) standard example, the GPU-based parallel NF algorithm is in the structured grid model and the equivalent 2.5-dimensional non- On the structured grid model, compared with the serial version of the NF method, the acceleration ratios of 19.8 and 17.0 times were obtained respectively; compared with the mainstream serial solution method, the efficiency was also improved by 2 to 3 times.

Economic Model-Based Controller Design Framework for Hydraulic Fracturing To Optimize Shale Gas Production and Water Usage

As water issues associated with hydraulic fracturing have received much attention, several optimization approaches have been developed for effective water management. However, most of them have not considered pumping schedules for hydraulic fracturing, which determine the productivity of a shale well as well as the total amount of freshwater required. Because of this, a novel model-based control framework is proposed for hydraulic fracturing to maximize the net profit from shale gas development which simultaneously minimizes the total cost associated with water management. The framework is as follows: initially a reduced-order model and a Kalman filter are developed based on the simulation data generated from a high-fidelity hydraulic fracturing model to correlate the pumping schedule and the final fracture geometry. Then, a numerical reservoir simulator and mixed-integer nonlinear programming model are used to generate two maps describing the revenue from selling shale gas produced and cost from managing wastewater recovered, respectively. Finally, by applying a data-based dynamic input–output model to connect the two maps, an economic model predictive control system is formulated. The proposed control framework enables 62% of the generated wastewater to be reused through the application of thermal membrane distillation technology in the treatment process and results in a 11% reduction in overall freshwater consumption, while maintaining the productivity of a shale well at its theoretical maximum.

Development of accurate well models for numerical reservoir simulation

Peaceman well model is widely used in numerical reservoir simulation. With the help of the defined equivalent radius, the bottom-hole inflow or outflow flux can be calculated and is proportional to the difference of the bottom-hole pressure and the well grid pressure. It is shown in this article, though the bottom-hole flux is calculated accurately in Peaceman well model, there are some significant errors of pressure near the well for the large value of the length-to-width ratio of the mesh. Two alternative methods, the source term compensation method and the pattern competition method, which are both based on the analytic solution induced by the source term, are proposed for homogeneous medium. In the source term compensation method, auxiliary pressure, which satisfies the Laplace equation strictly, is defined and solved instead of the original pressure variable, which actually satisfies the Poisson equation. In the pattern competition method, different flow patterns including the linear flow pattern and radial flow pattern are considered. Each flow pattern corresponds to a transmissibility of the adjacent two grids and all the transmissibilities are calculated respectively. The smallest transmissibility will outcompete, and be used for solving the discrete pressure equations. Numerical results show that for the two proposed methods, not only the bottom-hole flux but also the pressure fields can be calculated accurately. Cited as : Zhang, S., Liu, Z., Shi, A., Wang, X. Development of accurate well models for numerical reservoir simulation. Advances in Geo-Energy Research, 2019, 3(3): 250-257, doi: 10.26804/ager.2019.03.03

A novel numerical model of gas transport in multiscale shale gas reservoirs with considering surface diffusion and Langmuir slip conditions

AbstractMultiflow mechanisms coexist in shale gas reservoirs (SGRs) due to the abundant nanopores and the organic matter as a medium of gas souring and storage. The gas transport mechanisms in nanopores including bulk gas transfer and adsorption‐gas surface diffusion were already investigated in pore‐scale models, but their effects on actual gas production of multistage fractured horizontal wells in SGRs are not clearly understood, which are crucial for the economic development of unconventional resources. Therefore, a comprehensive apparent permeability (AP) model which couples the surface diffusion of adsorbed gas, slippage flow considering the additional flux generated by surface diffusion based on Langmuir's theory, and Knudsen diffusion is established. The presented model is validated with the experimental data and lattice Boltzmann method (LBM) simulation results. Then, we propose a numerical model which combines multiflow mechanisms in microscale pores and a multistage fractured horizontal well (MSFHW) in macroscale shale gas reservoirs together. The effects of different transport mechanisms on both AP of nanopores and gas production are analyzed thoroughly. The results show that the effect of surface diffusion on the apparent permeability of nanopores is much greater than that on the actual gas production of MSFHW, and the influence of high‐pressure condition must be considered when calculating the surface diffusion coefficient. The presented numerical model has important implications for accurate numerical simulation and efficient development of shale gas reservoirs.

Water alternating gas incremental recovery factor prediction and WAG pilot lessons learned

Water alternating gas (WAG) injection process is a proven enhanced oil recovery (EOR) technology with many successful field applications around the world. WAG pilot projects demonstrate that WAG incremental recovery factor typically ranges from 5 to 10% of original oil in place, though up to 20% has been observed in some fields. Despite its proven success, WAG application growth has been very slow. One of the reasons is the unavailability of robust analytical predictive tools that could estimate WAG incremental recovery factor, which is required for preliminary economic analysis before committing to expensive and time-consuming detailed technical studies and field pilot test that often requires a lot of input data. A semi-numerical model for WAG incremental recovery factor prediction was developed based on data mining of published WAG pilots to fill this gap. An extensive review of published WAG pilot projects was carried out, and consequently, 33 projects from 28 fields around the world were selected for this research study. Field WAG incremental recovery factor and parameters with total of one hundred and seventy-seven (177) observations were inputted to the predictive model. A predictive model was developed using both regression and group method of data handling (GMDH) techniques; 70% of the 33 WAG pilot projects data were used as validation, whereas remaining 30% of data set were used for validation. The predictive model results achieved with coefficient of determination (R2) from the regression method were ranging from 0.892 to 0.946 and 0.854 to 0.917 for training and validation sets, respectively. However, the prediction model coefficient of determination (R2) using GMDH method was ranging from 0.964 to 0.981 and 0.934 to 0.974 for training and validation, respectively. The developed predictive model can predict WAG incremental recovery factor versus multiple input parameters that include rock type, WAG process type, hydrocarbon pore volume of injected gas, reservoir permeability, oil gravity, oil viscosity, reservoir pressure, and reservoir temperature. The results of the study demonstrated that few input parameters have a significant impact on WAG incremental recovery factor as reservoir permeability and hydrocarbon pore volume of injected gas. This research study uses a novel approach in pre-defining the expected incremental WAG recovery factor before committing resources for building complex numerical reservoir simulation models and running WAG pilot tests, which are very time-consuming and costly and require extensive data input.

Impacts of boundary conditions on reservoir numerical simulation and performance prediction of enhanced geothermal systems

Fractured reservoir boundary conditions are crucial to numerical study on reservoir simulation and performance prediction of an enhanced geothermal system (EGS) but have received little attention in previous studies. We established an integrated model involving fractured reservoir and Double-flash geothermal power plant to compare heat extraction and power generation of the EGS under “recharge” and “no-flow” boundary conditions based on geothermal characteristics of Gonghe Basin, China. The “recharge” boundary conditions allow fluxes of mass and heat to cross reservoir boundaries, whereas the “no-flow” disallow such fluxes. The results show that the heat extraction potential around the periphery of the reservoir is overlooked under “no-flow” boundary conditions. Moreover, the fluxes of mass and heat across the reservoir boundaries exert limited influence on heat extraction process in initial stage, but they affect the process significantly in later stage. Additionally, compared with the EGS under “no-flow” boundary conditions, that under the “recharge” has a longer lifespan of 20%, and produces 8% more electricity power. It is also found the EGS maintains high-level power generation performance in the initial period under both boundary conditions, while the electricity output and thermal efficiency decrease significantly in the following stage.

New calculation method for condensate oil saturation of gas condensate reservoir

ABSTRACT During the cyclic gas injection of gas condensate reservoir with bottom or edge water, complicate phase changes and energy conversion take place. A new calculation model for condensate oil saturation is established based on the principle of volume conservation. Moreover, the calculation model considers the influencing factors such as retrograde condensation, connate water evaporation, elastic expansion of reservoir rocks, and external dynamic water invasion. Combining the reservoir fluid property and dynamic production data, this model can calculate the condensate oil saturation under different reservoir pressures quickly. The Case study shows that the condensate oil saturation obtained by this model coincides with the indoor depletion experiment and reservoir numerical simulation. Meanwhile, the sensitivity evaluation of the above influencing factors is carried out. Furthermore, the factors impacting the calculated results from high to low are external dynamic water invasion, elastic expansion of reservoir rocks and connate water evaporation.