Numerical Reservoir Simulation Research Articles

Mixed Horizontal Well Pattern and Fracturing Parameter Optimization of Low Permeability Reservoir

Horizontal well fracturing development is significant in improving the development effect of low permeability reservoirs and is widely applied in many low permeability reservoirs of China. In this study, the reservoir numerical simulation is used to establish different mixed horizontal well networks of the target reservoir. The result shows that the best waterflood development effect is achieved when horizontal segment orientation and crack orientation are at an angle of 45°. The horizontal segment length and fracturing parameters of mixed horizontal well networks are optimized. It is considered that the rational horizontal segment length of the horizontal well is 400 m, the rational number of cracks is 3, and the optimal crack conductivity is between 180-200 mD.m. Through the study of mixed horizontal well network and fracturing parameter optimization, the low permeability reservoir efficient horizontal well fracturing development is well guided.

Study on the Countermeasures and Mechanism of Balanced Utilization in Multilayer Reservoirs at Ultra-High Water Cut Period

After entering the ultra-high water cut stage of multilayer oil reservoirs, the remaining oil is highly dispersed. Due to the continuous development of general water injection, the generation of advantageous channels makes interlayer contradictions more prominent, and the differences in the utilization between different layers are even greater. After the water drive development of multilayer oil reservoirs enters the ultra-high water cut stage, the development effect deteriorates year by year. Layer restructuring is an effective method of improving the water injection development effect and increasing the degree of utilization. In essence, its goal is to achieve balanced utilization for multiple development layers to increase the degree of recovery. This article mainly employs physical simulation experiments combined with reservoir numerical simulation technology to jointly study the effects of different equilibrium production strategies in the ultra-high water cut period of multilayer oil reservoirs and their mechanism of action based on the remaining oil distribution field and streamline field. As a specific implementation, we use large-plate physical simulation to demonstrate the effectiveness of the rotational injection and production strategy, and to supplement the physical simulation experiment with a reservoir numerical simulation model, we analyze the mechanism of different balanced production strategies. The research results for the combination of physical simulation experiments and numerical simulation experiments show that the combined strategy of rotary injection and rotary production is the most effective method for use in multilayer and ultra-high water cut oil reservoirs. The displacement effect of the high-permeability layer is better, and the increase in the recovery degree is relatively large, while the displacement effect of the low-permeability layer is relatively weak. After conventional water drive oil recovery, the remaining oil mainly exists in the edge area of the research area. However, the use of three-dimensional well network injection wheel recovery changes the streamline field, produces the effect of fluid flow diversion, expands the water drive sweep coefficient, and improves the recovery rate. Chemical plugging can effectively replace water drive oil recovery and will become the main method for improving the recovery rate of such reservoirs in the lower part.

Feasibility of Advanced CO2 Injection and Well Pattern Adjustment to Improve Oil Recovery and CO2 Storage in Tight-Oil Reservoirs

Global tight-oil reserves are abundant, but the depletion development of numerous tight-oil reservoirs remains unsatisfactory. CO2 injection development represents a significant method of reservoir production, potentially facilitating enhanced oil recovery (EOR) alongside CO2 storage. Currently, limited research exists on advanced CO2 injection and well pattern adjustment aimed at improving the oil recovery and CO2 storage within tight-oil reservoirs. This paper focuses on the examination of tight oil within the Ordos Basin. Through the employment of slim-tube experiments, long-core displacement experiments, and reservoir numerical simulations, the near-miscible pressure range and minimum miscible pressure (MMP) for the target block were ascertained. The viability of EOR and CO2 sequestration via advanced CO2 injection was elucidated, establishing well pattern adjustment methodologies to ameliorate CO2 storage and enhance oil recovery. Simultaneously, the impacts of the injection volume and bottom-hole pressure on the development of advanced CO2 injection were explored in further detail. The experimental results indicate that the near-miscible pressure range of the CO2–crude oil in the study area is from 15.33 to 18.47 MPa, with an MMP of 18.47 MPa, achievable under reservoir pressure conditions. Compared to continuous CO2 injection, advanced CO2 injection can more effectively facilitate EOR and achieve CO2 sequestration, with the recovery and CO2 sequestration rates increasing by 4.83% and 2.29%, respectively. Through numerical simulation, the optimal injection volume for advanced CO2 injection was determined to be 0.04 PV, and the most favorable bottom-hole flowing pressure was identified as 10 MPa. By transitioning from a square well pattern to either a five-point well pattern or a row well pattern, the CO2 storage ratio significantly improved, and the gas–oil ratio of the production wells also decreased. Well pattern adjustment effectively supplements the formation energy, extends the stable production lives of production wells, and increases both the sweep efficiency and oil recovery. This study provides theoretical support and serves as a reference for CO2 injection development in tight-oil reservoirs.

Progress and Challenges of Integrated Machine Learning and Traditional Numerical Algorithms: Taking Reservoir Numerical Simulation as an Example

Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges.

Numerical Modeling and Simulation of Fractured-Vuggy Reservoirs Based on Field Outcrops

We propose a novel workflow to investigate the complex flow behaviors and remaining oil distribution related to the oil–gas–water three-phase system based on information from typical outcrops of fractured-vuggy reservoirs. A refined geological model is built to represent the size, geometry, and spatial distribution of the karst caves and fractures extracted from the field outcrop photographs. The combination of the perpendicular bisector (PEBI) grid technique and the control-volume finite difference method is adopted for space discretization. We have validated the numerical model against experimental data. Numerical simulations were performed to explore the impacts of the permeability of karst cave and natural fractures and the position of natural water bodies upon oil production performance. Numerical results indicate that (1) the cave permeability has few impacts on the oil production, yet the fracture permeability plays a significant role in determining the oil recovery; (2) a higher permeability of the fractures will lead to a longer period of time for no-water oil production and, thus, a higher oil recovery; (3) the position of natural water body shows significant impacts on oil recovery, e.g., a short distance between the natural water body and the production well tends to form preferential passages, causing severe reduction of water flooding range; and (4) the distribution of remaining oil is controlled by spatial patterns of the fractured-vuggy system and reservoir development schemes. We found that the remaining oil is mainly distributed along the model boundaries and at the corner of the caves with single or multiple connection/s to fractures.

Generalized finite difference method-based numerical modeling of oil–water two-phase flow in anisotropic porous media

This paper applies generalized finite difference method (GFDM) to a compressible two-phase flow in anisotropic porous media with the aim of further extending the wider application of this class of meshless methods. We develop an implicit Euler scheme in time and a GFDM discretization in space based on two treatments of the anisotropic permeability tensor in continuous function expression and discrete distribution. The effectiveness and generality of GFDM for two-phase flow problems in anisotropic porous media are verified by three examples with rectangular, irregular, and complex boundaries. Also, the computational performance of the method is verified according to the error calculation with L2 absolute error functions in different node collocation schemes. In addition, the sensitivity analysis of the radius of the influence domain to the transient pressure equation (parabolic equation) and the saturation equation (hyperbolic equation) is considered. It generally holds that the larger the radius of the influence domain, the lower the calculation accuracy in the case of Cartesian collocation. This may be a preliminary rule for the radius choice of the influence domain for GFDM. In sum, this work provides an efficient and accurate meshless solver to handle two-phase flow problems in anisotropic porous media under the GFDM framework, which reveals the great application potential of GFDM in reservoir numerical simulation.

Research Methods and Prospects of Oil and Gas Reservoir Numerical Simulation

This paper briefly describes the development process of oil and gas reservoir numerical simulation, introduces the method and application of oil and gas reservoir numerical simulation, and predicts the future development trend of oil and gas reservoir numerical simulation on this basis, To help extract oil better and more efficiently.

Gas generation potential and pore characteristics of Jurassic lacustrine shale containing type Ⅱ kerogen: A case study of the Yabulai Basin, northwestern China

Jurassic lacustrine shales containing kerogen types Ⅱ and Ⅲ are widely distributed in sedimentary basins around the world. Previous studies on gas generation potential and pore characteristics of Jurassic lacustrine shales mainly focused on type Ⅲ kerogen, but ignored the systematic study of the shales containing type Ⅱ kerogen. This led to a poor understanding of the gas generation potential and pore characteristics of Jurassic lacustrine shales, which limits the effective exploration and development of shale gas. Taking the Lower Xinhe (J2x1) shale of the Yabulai Basin in northwestern China as the research object, this paper examines the gas generation potential and pore characteristics of a Jurassic lacustrine shale containing type Ⅱ kerogen. The J2x1 shale is largely classified as a very good source rock, kerogen is predominantly type Ⅱ, with thermal maturity ranging from mature to highly over-mature. The J2x1 shale is characterized by low porosity and permeability, and high content of brittle minerals. Different from the Jurassic lacustrine shales containing type Ⅲ kerogen, the J2x1 shales have a large amount of interparticle and intraparticle dissolution pores in the clay minerals as well as organic pores. The porosity and pore structure of the J2x1 shale are strongly controlled by total organic carbon (TOC) content. The methane adsorption capacity of the J2x1 shale is in the range of 2.10–2.99 m3/t, which is controlled by TOC content, clay mineral content, temperature and pressure. The geochemical characteristics, mineral composition and adsorption capacity suggest that the J2x1 shale has good shale exploration and development potential. A mathematical model is established to describe the various characteristics of methane adsorption capacity with depth and TOC content. The methane adsorption capacity of the J2x1 shale first increases and then decreases with increasing depth, with the depth corresponding to the maximum methane adsorption increases with increasing TOC content. When the TOC values of the J2x1 shale are mainly between 1% and 10%, the corresponding depth of the maximum methane adsorption capacity is 400–800 m, and the adsorption amount of the shale tends to minimize at a depth range of 2600–5100 m. The favorable shale gas exploration depth of the J2x1 shale in the study area increases with increasing TOC content. This research provides a geological reference for the exploration of this Jurassic shale gas in the Yabulai Basin, and the relevant experimental data provide important parameters for the numerical simulation of shale gas reservoirs in the study area. Moreover, the results of this study also provide an important reference for other basins in northwestern China, as well as continental basins with similar geological conditions in other regions.

Development of a hydrate risk assessment tool based on machine learning for CO2 storage in depleted gas reservoirs

Depleted gas reservoirs are attractive sites for Carbon Capture and Storage (CCS) due to their huge storage capacities, proven seal integrity, existing infrastructure and subsurface data availability. However, CO2 injection into depleted formations can potentially lead to hydrate formation near the wellbore due to Joule-Thomson cooling, which might cause injectivity issues. Some challenges encountered when modeling and simulating this process are the computational time caused by Newton’s convergence issues and instability. The objective of this work is to propose a novel approach for hydrate risk assessment during CO2 injection into depleted gas reservoirs using physics-based Machine Learning (ML) approach. First, the selection of input parameters for the ML models is performed based on sensitivity study results using an analytical solution for different operational and petrophysical values. Then the ML models are tuned and tested using datasets from numerical reservoir simulation results based on a wide range of input parameter values. To the best of our knowledge, this is the first time that an ML approach is used for risk assessment of CO2 hydrate in its storage in depleted gas reservoirs. The ML models developed in this study presented an efficient performance to predict hydrate-forming events. The deep neural network model performed best with a 95% recall value and 84% precision value. These results show that the ML model can be further utilized for risk assessment in the screening stage, and the combination of screening by ML, followed by detailed analysis with numerical simulation in high-risk cases can be an efficient probing workflow for future CCS projects.

Machine-Learning-Based Approach to Optimize CO2-WAG Flooding in Low Permeability Oil Reservoirs

One of the main applications of carbon capture, utilization, and storage (CCUS) technology in the industry is carbon-dioxide-enhanced oil recovery (CO2-EOR). However, accurately and rapidly assessing their application potential remains a major challenge. In this study, a numerical model of the CO2-WAG technique was developed using the reservoir numerical simulation software CMG (Version 2021), which is widely used in the field of reservoir engineering. Then, 10,000 different reservoir models were randomly generated using the Monte Carlo method for numerical simulations, with each having different formation physical parameters, fluid parameters, initial conditions, and injection and production parameters. Among them, 70% were used as the training set and 30% as the test set. A comprehensive analysis was conducted using eight different machine learning regression methods to train and evaluate the dataset. After evaluation, the XGBoost algorithm emerged as the top-performing method and was selected as the optimal approach for the prediction and optimization. By integrating the production prediction model with a particle swarm optimizer (PSO), a workflow for optimizing the CO2-EOR parameters was developed. This process enables the rapid optimization of the CO2-EOR parameters and the prediction of the production for each period based on cumulative production under different geological conditions. The proposed XGBoost-PSO proxy model accurately, reliably, and efficiently predicts production, thereby making it an important tool for optimizing CO2-EOR design.

Optimization of shale gas fracturing parameters based on artificial intelligence algorithm

Resource-rich shale gas plays a pivotal role in new energy types. The key to scientifically and efficiently developing shale gas fields is to clarify the main factors that affect the production of shale gas wells. In this paper, according to the shale gas reservoir characteristic of the Fuling marine Longmaxi Formation, a single-well geological model was established using the reservoir numerical simulation software CMG. Then, 10,000 different reservoir models were randomly generated for different formation physical parameters, completion parameters, and fracturing parameters using the Monte Carlo method, and these 10,000 models were simulated numerically. The machine learning model uses a dataset of 10,000 different geological, completion, and fracturing parameters as input and 10,000 production curves as output. Multiple machine learning regression methods were used to train and test the dataset, and the optimal method (GBDT algorithm) was selected, and the accuracy R2 of the test set of the GBDT prediction model is 0.96. A fracturing parameter optimization workflow was constructed by combining a production prediction model with a particle swarm optimizer (PSO). The process can quickly optimize the fracturing parameters and predict the production for each time by targeting the cumulative gas production under different geological conditions. The optimized parameters are Fracture Spacing, Fracture Width, Intrinsic Permeability, Fracture Half-length, Langmuir Pressure, and Langmuir Volume. The initial predicted cumulative gas production was 4.59 × 108 m3, which was optimized to 4.90 × 108 m3. The proposed PSO-GBDT proxy model can instantly predict the production of shale gas wells with considerable accuracy, reliability, and efficiency, which is a vital tool for optimizing fracture design. This investigation provides a solid foundation for predicting the production of unconventional gas reservoirs and for parameter optimization.

Development Adjustment Scheme of a Low-Permeability Reservoir in the SN Oilfield

Due to the complexity of low-permeability reservoirs, there is no set of established plans to adjust the development of specific low-permeability reservoirs, and the final development adjustment plan should be determined through comprehensive consideration of many aspects. At present, the development of low-permeability reservoirs in the SN oilfield faces problems. It is urgent to formulate corresponding development adjustment plans to improve the current development situation. In view of the problems of the current development surface of low-permeability reservoirs in the SN oilfield, this paper used the previous research results of a low-permeability reservoir development adjustment for reference, evaluated the reservoir development effect, analyzed the remaining oil distribution via numerical reservoir simulation, and finally designed and optimized a development adjustment plan. The research results show that the injection–production ratio and pressure of low-permeability reservoirs in the SN oilfield were generally maintained, the oil production decreased, the water cut increased, and the remaining recoverable reserves were considerable; the historical matching quality of a reservoir numerical simulation was good. There was more remaining oil in the northeast of the J007 well block and the north of the J008 well block, and the potential of the L2 and L3 layers was great. The remaining oil was mainly concentrated in the areas with high positions, poor physical properties, and imperfect well patterns; the predicted cumulative oil production of Scheme IV in 15 years and recovery were the highest, with the best development effect and the highest economic benefit. This was determined as the development adjustment plan for low-permeability reservoirs in the SN oilfield. The research results of this paper aim to improve oil production, the ultimate recovery factor, and economic benefits of low-permeability reservoirs in the SN oilfield, and to provide a reference for the development of similar oilfields.

Intelligent Optimization of Gas Flooding Based on Multi-Objective Approach for Efficient Reservoir Management

The efficient development of oil reservoirs mainly depends on the comprehensive optimization of the subsurface fluid flow process. As an intelligent analysis technique, artificial intelligence provides a novel solution to multi-objective optimization (MOO) problems. In this study, an intelligent agent model based on the Transformer framework with the assistance of the multi-objective particle swarm optimization (MOPSO) algorithm has been utilized to optimize the gas flooding injection–production parameters in a well pattern in the Middle East. Firstly, 10 types of surveillance data covering 12 years from the target reservoir were gathered to provide a data foundation for model training and analysis. The prediction performance of the Transformer model reflected its higher accuracy compared to traditional reservoir numerical simulation (RNS) and other intelligent methods. The production prediction results based on the Transformer model were 21, 12, and 4 percentage points higher than those of RNS, bagging, and the bi-directional gated recurrent unit (Bi-GRU) in terms of accuracy, and it showed similar trends in the gas–oil ratio (GOR) prediction results. Secondly, the Pareto-based MOPSO algorithm was utilized to fulfil the two contradictory objectives of maximizing oil production and minimizing GOR simultaneously. After 10,000 iterations, the optimal injection–production parameters were proposed based on the generated Pareto frontier. To validate the feasibility and superiority of the developed approach, the development effects of three injection–production schemes were predicted in the intelligent agent model. In the next 400 days of production, the cumulative oil production increased by 25.3% compared to the average distribution method and 12.7% compared to the reservoir engineering method, while GOR was reduced by 27.1% and 15.3%, respectively. The results show that MOPSO results in a strategy that more appropriately optimizes oil production and GOR compared to some previous efforts published in the literature. The injection–production parameter optimization method based on the intelligent agent model and MOPSO algorithm can help decision makers to update the conservative development strategy and improve the development effect.

Study on Interaction Characteristics of Injected Natural Gas and Crude Oil in a High Saturation Pressure and Low-Permeability Reservoir

Natural gas injection is considered for enhanced oil recovery (EOR) in a high saturation pressure reservoir in block B111 of the Dagang oilfield, China. To investigate the interaction characteristics of injected natural gas and crude oil, the ability for dissolution–diffusion and miscibility–extraction of natural gas in crude oil was tested using a piece of high-temperature and high-pressure PVT equipment. The physical properties and minimum miscible pressure (MMP) of the natural gas–crude oil system and their interaction during dynamic displacement were analyzed using the reservoir numerical simulation method. The results show the following: (1) Under static gas–oil contact conditions, natural gas has a significant dissolution–diffusion and miscibility–extraction effect on the crude oil in block B111, especially near the gas–oil interface. The content of condensate oil in gas phase is 10.14–18.53 wt%, while the content of dissolved gas in oil phase reaches 26.17–57.73 wt%; (2) Under the reservoir’s conditions, the saturated solubility of natural gas injected in crude oil is relatively small. The effect of swelling and viscosity reduction on crude oil is limited. As the pressure increases with more natural gas dissolved in crude oil, the phase state of crude oil can change from liquid to gas; accordingly, the density and viscosity of crude oil will be greatly reduced, presenting the characteristics of condensate gas; (3) The MMP of natural gas and crude oil is estimated to be larger than 40 MPa. It mainly forms a forward-contact evaporative gas drive in block B111. The miscible state depends on the maintenance level of formation pressure. The injected natural gas has a significant extraction effect on the medium and light components of crude oil. The content of C2–C15 in the gas phase at the gas drive front, as well as the content of CH4 and C16+ in the residual oil at the gas drive trailing edge, will increase markedly. Accordingly, the residual oil density and viscosity will also increase. These results have certain guiding significance for understanding gas flooding mechanisms and designing gas injection in block B111.

Characterization of Flow Parameters in Shale Nano-Porous Media Using Pore Network Model: A Field Example from Shale Oil Reservoir in Songliao Basin, China

The pore-throat radius of the shale oil reservoir is extremely small, and it is difficult to accurately obtain the absolute permeability and oil–water two-phase relative permeability of the actual oil reservoir through conventional core experiments. However, these parameters are very important for reservoir numerical simulation. In this paper, a method for characterizing flow parameters based on a pore network model that considers differential pressure flow and diffusion flow is proposed. Firstly, a digital core was reconstructed using focused ion beam scanning electron microscopy (FIB-SEM) from the Gulong shale reservoir in the Songliao Basin, China, and a pore network model was extracted. Secondly, quasi-static single-phase flow and two-phase flow equations considering diffusion were established in the pore network model. Finally, pore-throat parameters, absolute permeability, and oil–water two-phase permeability curves were calculated, respectively. The results show that the pore-throat distribution of the Gulong shale reservoir is mainly concentrated in the nanometer scale; the mean pore radius is 87 nm, the mean throat radius is 41 nm, and the mean coordination number is 3.97. The calculated permeability considering diffusion is 0.000124 mD, which is approximately twice the permeability calculated without considering diffusion. The irreducible water saturation of the Gulong shale reservoir is approximately 0.4, and the residual oil saturation is approximately 0.35. The method proposed in this paper can provide an important approach for characterizing the flow parameters of similar shale oil reservoirs.

Effect of Physical Properties on Mechanical Behaviors of Sandstone under Uniaxial and Triaxial Compressions.

Mechanical properties of sandstone, such as compressive strength and young's modulus, are commonly used in the design of geotechnical structures and numerical simulation of underground reservoirs using models such as the digital groundwater, equivalent porous medium, and Discrete Fracture Network (DFN) models. A better understanding of the mechanical behaviors of sandstone under different loading conditions is imperative when assessing the stability of geotechnical structures. This paper highlights the effect of the physical properties (i.e., porosity, mean grain size) and environmental conditions (i.e., water content and confining stress) on uniaxial compressive strength, triaxial compressive strength, and young's modulus of sandstone. A series of uniaxial and triaxial compression experiments are conducted on sandstone formations from Wyoming. In addition, experimental data on sandstones from the literature are compiled and integrated into this study. Prediction equations for the compressive strengths and young's modulus of sandstone are established based on commonly available physical properties and known environmental conditions. The results show that the mean Uniaxial Compressive Strength (UCS) decreases as the porosity, water content, and mean grain size increase. Furthermore, a predictive empirical relationship for the triaxial compressive strength is established under different confinements and porosity. The relationship suggests that the mean peak compressive strength increases at a higher confinement and decreases at a higher porosity. The results and recommendations provide a useful framework for evaluating the strength and deformation of most sandstone.

A Transient Productivity Prediction Model for Horizontal Wells Coupled with Oil and Gas Two-Phase Seepage and Wellbore Flow

Capacity prediction is the basis for the optimization of oil and gas well production work systems and parameter optimization design. Horizontal wells are becoming increasingly popular for oil and gas extraction. However, the seepage law of reservoirs produced with horizontal wells is more complicated than that of reservoirs produced with vertical wells, especially when the bottom hole flowing pressure or formation pressure is less than the saturation pressure of crude oil in the reservoir. Oil and gas two-phase seepage can occur in a part or all areas of the wellbore and reservoir. Because the oil and gas two-phase seepage characteristics of reservoir oil well production will be reduced—possibly greatly reduced—the formation seepage law is complex. Thus, it is very important to better predict the horizontal well capacity. To address this, a method and process of establishing a transient calculation model of two-phase flow in horizontal wells are introduced in detail from three aspects: fluid physical properties, reservoir oil and gas two-phase seepage, and the coupling model of the inflow performance and flow in the wellbore. The model is found to be reliable through verification with production data from five wells in two oilfields. The established model simplifies the reservoir model, does not involve very complex meshing, and only simulates one well. Therefore, the calculation speed will be faster than that of other reservoir numerical simulation methods under the same conditions.

Field pilot testing and reservoir simulation to evaluate processes controlling CO2 injection and associated in-situ fluid migration in deep coal

A key strategy for reducing global CO2 emissions is geologic sequestration of CO2. Deep, unmineable coal seams have been proposed as possible targets for CO2 injection and storage because of the global distribution of coal resources, and relative security of CO2 storage associated with CO2 adsorption. However, the current paradigm is that near-injector permeability loss caused by pure CO2 adsorption-induced coal swelling limits the effectiveness of this process making coal a less than an ideal target for CO2 injection/sequestration.In order to test the viability of CO2 injection and storage in deep coal seams, and evaluate processes controlling CO2 injection, migration and storage therein, a field pilot was implemented in the Mannville Coal (∼1500 m depth) of Alberta, Canada. An additional objective of the field pilot was to quantify the migration of water and natural gas away from the CO2 injection well, an important mechanism used to improve the performance of the pilot operator's enhanced hydrogen recovery (EHR™) process. To achieve these multiple objectives, a vertical injection well and closely-spaced offsetting vertical observation well were drilled, cored, and completed in the Mannville coal seams, and instrumented with pressure sensors to monitor both the coal and bounding zones during injection testing. Periodic fluid sampling at the observation well allowed for quantification of CO2 plume and fluid migration. Pre-CO2 injection/falloff testing was performed with water at variable injection rates/pressures to assess coal permeability/porosity changes without the complication of CO2-coal interaction. CO2 injection/falloff tests were then performed at increasing CO2 injection rates to assess the combined impact of injection pressure/CO2 rate and CO2-coal interaction (e.g., adsorption) on injectivity. All tests were history-matched with a numerical reservoir simulator; the calibrated model was used to study the key controls on CO2 storage, CO2 plume migration, in-situ fluid displacement, and injectivity.A key finding from simulation of the pilot results is that, for the Mannville coals in the study area, permeability increases caused by coal natural fracture dilation during injection outweigh permeability decreases caused by CO2 adsorption-induced coal swelling, resulting in an injectivity index increase with CO2 injection rate. In addition, the coals exhibit strong anisotropy in permeability and geomechanical properties; ignoring coal anisotropy in the simulation model causes coal swelling effects to be overestimated, resulting in an underestimate in CO2 plume extent.From the pilot results and analysis, it can be concluded that the deep Mannville coal could be a viable target for CO2 storage.

Strategizing well locations in unconfined, heterogeneous hydrate reservoirs for maximizing gas production

Natural gas hydrates are a potential alternative to conventional energy due to their high energy density and low carbon with wide distribution across the globe. Producing methane from the gas hydrate reservoirs is technically challenging as they are present in complex geological environments which are highly heterogeneous. In this work, using numerical reservoir simulations of gas production from oceanic gas-hydrate reservoirs underlain with an aquifer we show that warm-water injection is necessary when the water layer below the hydrates is unconfined. Our simulations reveal that the aquifer characterization is essential to design the gas production strategy and estimating the gas recovery. We demonstrate that for a gas-hydrate reservoir attached to a moderately unconfined aquifer, warm-water injection in the hydrate-zone leads to more recovery. If the hydrate-zone is layered then the gas recovery improves by injecting the water into a more porous layer. However, for highly unconfined reservoirs water should be injected near the aquifer for efficient recovery of gas. Our findings will help in developing gas production plans from the hydrate reservoirs around the world.

New Correlation for Predicting Undersaturated Oil Compressibility for Mishrif Reservoir in the Southern Iraqi Oil Fields

Reservoir fluids properties are very important in reservoir engineering computations such as material balance calculations, well testing analyses, reserve estimates, and numerical reservoir simulations. Isothermal oil compressibility is required in fluid flow problems, extension of fluid properties from values at the bubble point pressure to higher pressures of interest and in material balance calculations (Ramey, Spivey, and McCain). Isothermal oil compressibility is a measure of the fractional change in volume as pressure is changed at constant temperature (McCain). The most accurate method for determining the Isothermal oil compressibility is a laboratory PVT analysis; however, the evaluation of exploratory wells often require an estimate of the fluid behavior prior to obtaining a representative reservoir sample. Also, experimental data is often unavailable.Empirical correlations are often used for these purposes.This paper developed a new mathematical model for calculating undersaturated oil compressibility using 129 experimentally obtained data points from the PVT analyses of 52 bottom hole fluid samples from Mishrif reservoirs in the southern Iraqi oil fields. The new undersaturated oil compressibility correlation developed using Statistical Analysis System (SAS) by applying nonlinear multiple regression method. It was found that the new correlation estimates undersaturated oil compressibility of Mishrif reservoir crudes in the southern Iraqi oil fields much better than the published ones. The average absolute relative error for the developed correlation is 7.16%.