Commercial Reservoir Simulator Research Articles

Fluid and rock heterogeneity assessment of gas condensate reservoirs by wavelet transform of pressure-transient responses

The response of gas condensate reservoirs is directly influenced by heterogeneity. In such reservoirs, condensate is created around wellbore areas, when the reservoir pressure falls below the dew point pressure. Consequently, the distribution of this condensate bank makes fluid flow in the reservoir even more complicated by changing rock-fluid properties (e.g., relative permeability). This alteration can be assumed as a new heterogeneity, called fluid heterogeneity. The separation of fluid heterogeneity from rock heterogeneity is a challenging task. The main idea of this study is to investigate the transient pressure responses of a gas condensate reservoir to separate rock and fluid heterogeneities. Different homogeneous and heterogeneous reservoir models of a reservoir were constructed by the geostatistical approach. A commercial reservoir simulator was used to simulate the behavior of different drawdown and buildup scenarios. The fluid is a lean gas condensate selected from one of the Middle East formations. The wavelet transform (WT) approach was implemented to characterize the behavior of condensate banks for both homogeneous and heterogeneous models. Analyzing the wavelet approximate and detail coefficients of the pressure-transient responses enabled us to distinguish the rock and fluid heterogeneities. The results showed that the wavelet detail coefficient could be a good indicator of reservoir heterogeneity. Moreover, the WT could be successfully used to distinguish different regions of a condensate bank inside the reservoir under study. The implemented approach showed promising results in analyzing the pressure-transient responses of gas condensate reservoirs.

Three-Dimensional Projection-Based Embedded Discrete-Fracture Model for Compositional Simulation of Fractured Reservoirs

Summary Most unconventional oil and gas reservoirs are known to have several natural fractures in different orientations, which are consistent with the prevailing stresses when they were created. The accurate and efficient modeling of natural and hydraulic fractures presents a significant computational challenge. In this work, we show the limitations of the embedded discrete-fracture model (EDFM) and present the first 3D projection-based EDFM (pEDFM) algorithm and compositional simulation studies with realistic fracture networks in a fully 3D space. The simulation results from this work indicate that the pEDFM presented can model realistic fractured unconventional reservoirs accurately and efficiently. To validate the model, we present some simplistic fracture cases that can be meshed and modeled easily using explicit-fracture modeling in commercial-reservoir simulators. From the cases studied, we observe that using progressively finer grids near the hydraulic-fracture surfaces helps to improve model accuracy because this allows us to capture the sharp pressure drops expected near these fracture surfaces. The simulation results show that, unlike EDFM, the robust pEDFM algorithm presented here is accurate even at the low fracture-conductivity values expected in many of these ubiquitous natural fractures. In this paper, we present the first full 3D compositional modeling with pEDFM. We demonstrate that our model can accurately and efficiently model multiply fractured horizontal wells in unconventional reservoirs, which have complex networks of thousands of fractures at various orientations.

Numerical Analysis of the Effects of Apparent-Permeability Modeling and Secondary-Fracture Distribution for Hydraulic-Fractured Shale-Gas Production Analysis

Summary Complex flow mechanisms, such as Knudsen diffusion, are encountered in the shale matrix because of the presence of nanopores. Numerous apparent-permeability models have been proposed to capture the ensuing non-Darcy flow behavior. However, these models are not readily available in most commercial reservoir simulators, and ignoring these mechanisms can potentially underestimate the overall matrix conductivity. This work implements an explicit coupling strategy for integrating a pressure-dependent apparent-permeability model in reservoir simulation. The numerical models are subsequently used to study the effects of apparent-permeability modeling and natural-fracture distribution on gas production and water loss during flowback. The effects of multiphase-flow functions on fluid retention are also assessed. A set of 3D reservoir models are constructed using field data obtained from the Horn River shale-gas reservoir. First, stochastic 3D discrete-fracture-network (DFN) models are scaled up into equivalent continuum dual-porosity/dual-permeability models. An apparent-permeability (Kapp) model accounting for contributions of slip flow, Knudsen diffusion, and surface pore roughness is applied at each gridblock. A novel coupling scheme is formulated to facilitate the updating of Kapp after a certain specified time interval, capturing the pressure dependency of the Kapp. The sensitivity of the updating frequency is analyzed. The results reveal that incorporating these additional flow mechanisms by means of the apparent-permeability formulation could potentially increase the overall gas-production prediction by up to 11%, depending on the average pore radius, reservoir pressure, and several other matrix or fluid properties. The implications of Kapp modeling in water-loss mechanisms are further examined through a set of sensitivity analyses, where the effects of multiphase-flow functions and DFN distributions are systematically investigated. The following interesting findings are observed: Ignoring Kapp modeling could overestimate water recovery. Fracturing-fluid propagation and long-term water recovery are strongly affected by the secondary-fracture intensity; increase in secondary-fracture intensity would enhance water loss during flowback. Gas production is highly affected by the amount of water in the near-well region. In a gas/water system, compressibility of the in-situ fluids renders the effects of countercurrent imbibition and water retention to be more complex from those observed in similar water/oil systems. This work offers a novel, yet practical, scheme for representing the pressure-dependent matrix apparent permeability in the flow simulation of shale reservoirs. The proposed method captures the non-Darcy flow behavior caused by the complex transport mechanisms occurring in nanosized pores. Most importantly, this coupling procedure can be implemented in existing commercial reservoir-simulation packages. The results have revealed a few interesting insights regarding the potential implications in fracturing design and estimation of stimulated reservoir volume.

Numerical investigation of water retention in secondary fractures and apparent permeability modeling in shale gas production

During the hydraulic fracturing process, the fracturing fluid may cause water retention, if the nearby secondary fractures subsequently close and get disconnected due to changes in effective stress distribution during flowback and production. The circumstances and detailed mechanisms associated with this phenomenon are still poorly understood. In this work, a coupling scheme for incorporating a pressure-dependent apparent permeability model in reservoir simulation is implemented. The numerical models are subsequently used to investigate the impacts of water retention and apparent permeability modeling on gas production and water flowback.A high-resolution 3D reservoir model is constructed based on the field data obtained from the Horn River shale gas reservoir. Stochastic 3D discrete fracture netsork (DFN) model is upscaled into equivalent continuum dual-porosity dual-permeability (DPDK) model by analytical techniques. An apparent permeability (Kapp) model is employed to model transport mechanisms in nano-sized pore systems. In order to capture the pressure dependency, a novel coupling scheme is developed to facilitate the updating of Kapp and effective stress after a certain designated time interval. In addition, a novel method involving rock-type indicators is introduced to represent the open and closed states of secondary fractures, facilitating the modeling of stress-dependent closure of the secondary fracture system. Two secondary fracture closure behaviors (i.e., abrupt closure and gradual closure) are considered in our study.The results indicate that fracture closure would affect the gas production and water recovery, particularly if the near-well fractures are disconnected; the effect would be further exaggerated for a denser fracture network; fracture closure would also affect the matrix water retention near the well. Neglecting the effects of Kapp could essentially overestimate the contribution of hydraulic fracture for a certain observed gas production. The existence of secondary fractures could also enhance water loss during flowback. It is concluded that gas and water production would increase if less water is imbibed into the matrix during the shut-in period in the presence of disconnected secondary fractures. It is also observed that a shorter shut-in period may be beneficial to both water and gas recovery.This work presents a novel, yet practical, scheme for coupling stress-dependent matrix apparent permeability and fluid flow, as well as modeling pressure-dependent fracture closure. This modeling scheme can be readily integrated in most commercial reservoir simulation packages. The results have revealed several potential scenarios of water loss, along with the associated implications on optimal operational strategies and estimation of stimulated reservoir volume.

Prediction of Multilateral Inflow Control Valve Flow Performance Using Machine Learning

Summary Intelligent multilateral well completions provide downhole flow rate, pressure, and temperature measurements at multiple well segments, which allows for a continuous spatiotemporal data stream. Such an extensive data input poses a challenging task to decide on the optimal strategy of manipulating the inflow control valve (ICV) settings over time for best performance. In this study, we investigate using machine learning to analyze and predict well performance given different ICV settings to ultimately maximize the well output. A commercial reservoir simulator was used to generate two synthetic reservoir models: homogeneous (Case A) and heterogeneous (Case B). These synthetic data were used to train, validate, and test machine learning models. The reservoir cases were generated on the basis of a segmented, trilateral producer completed with three ICV devices installed at tie-in segments. The data used were measurements of wellhead and downhole flow rates across ICV segments over a period of 4,000 days. A total of 1,330 experiments were conducted with an 8-day timestep, generating a total of 667,660 sample data points for each of Case A and Case B. Fully connected neural networks were used to fit the data, while model generalizability was enhanced using regularization techniques, namely L2 regularization and early stopping. Both random sampling and Latin hypercube sampling (LHS) methods were evaluated in constructing the training, validation, and testing splits. Trained with different sample sizes drawn from the 1,330 simulated data histories for the two reservoir models, the proposed neural network showed excellent results. Given only 10 simulated choices of ICV settings for training, the network proved capable of predicting oil/water production profiles at surface for both homogeneous and heterogeneous reservoir models with greater than 0.95 coefficient of determination (R2) when evaluated at unseen, test ICV settings. Extending the problem to downhole flow performance prediction, approximately 40 training simulated settings were necessary to achieve 0.95 R2. We observed that LHS was superior to random sampling in both R2 average and confidence interval. We also found that increasing the training and validation sample sizes increased the test R2 when testing against unseen cases. Study results suggest the applicability of machine reinforcement learning to estimate the well output at different ICV settings, where the neural network model depends fully on the real-time well feedback and production measurements. By using a machine learning approach during the operation of a well with multiple ICV settings, it would be feasible to estimate the lateral-by-lateral output at unseen scenarios. Hence, it becomes possible to maximize the well output by using an optimization algorithm to determine the optimal ICV settings.

A modified pressure-gradient-based (PGB) sand failure criterion for dynamically and preferentially characterizing wormhole growth and propagation during CHOPS processes

In this paper, a recently developed pressure-gradient-based (PGB) sand failure criterion has been modified to improve its applicability for accurately quantifying the dynamic and preferential wormhole growth and propagation during cold heavy oil production with sand (CHOPS) processes. More specifically, the resistance of sand mobilization is analyzed and mechanical balance at pore-scale is established. The heterogeneous porosity distribution is newly integrated into the PGB sand failure criterion to identify the preferential directions of wormhole propagation. The sand failure is quantitatively examined by comparing the pressure gradients along with the potential directions at a threshold pressure gradient. Such an improved PGB sand failure criterion has been incorporated with a commercial reservoir simulator, in which three sets of relative permeability are utilized to represent the three physically distinct stages of the CHOPS processes. Subsequently, the newly modified PGB sand failure criterion is validated by laboratory sand production experiments and then extended to a field application. Good agreements between the simulated and measured data (i.e., production profiles and pressure) have been achieved from the laboratory experiments. As the preferential directions of the wormhole propagation can be dynamically determined through pressure gradient and porosity distribution, the simulated wormholes are much closer to the experimental observations than the previous work (Fan et al., 2019). The accuracy of the simulated production profiles has also been significantly improved by using three sets of permeability to separately represent the dominant impacts (i.e., sand failure, slurry flow, and boundary effect) on different stages of the CHOPS processes. In addition, good agreements between the simulated and measured data have been obtained from the field case by use of the improved PGB sand failure criterion, demonstrating its robustness and versatile applicability of predicting the dynamic and preferential wormhole network under various laboratory and field conditions.

Variation in b-sigmoids with flow regime transitions in support of a new 3-segment DCA method: Improved production forecasting for tight oil and gas wells

This study uses a commercial reservoir simulator to generate production rate data for shale wells with systematic variations in fracture treatment design parameters using a plausible range of reservoir properties. The synthetic well data is then used to generate diagnostic log-rate log-time plots, which allow for a distinction of four types of flow regimes. Such a distinction of flow regimes is of practical relevance, because of the inverse link with reservoir properties and fracture treatment design parameters. Each flow regime has a characteristic slope, which traditionally has been attributed to typical flow conditions in the reservoir. The synthetic production data from the reservoir simulator are used to constrain b-sigmoid patterns for narrow discrete time increments of production (1 month) throughout the various flow regimes occurring during the economic life of each synthetic well model. Nearly a hundred different models were generated as a basis for our analysis. From the analysis of the DCA parameters for time series, using the difference in inverse of decline rates (loss ratio) for two adjacent time steps, it appears possible to recognize systematic shifts in the instantaneous decline rates and temporal b-values. In particular, the generated b-value patterns (b-sigmoids) appear diagnostic for flow regime changes recognized on diagnostic log-rate log-time plots. The results demonstrate that it is possible, based on well design and reservoir parameters, to establish correlation trends between b-sigmoids and flow regime changes using time series of the DCA parameters. The results are subsequently used to improve the accuracy of a modified 3-segment DCA method based on Arps equation. The proposed method is applied to synthetic production data, generated based on an Eagle Ford shale oil well (with known reservoir and fracture properties), to demonstrate the practical value for production forecasting and reserves estimation. This research paper paves the way for wider, future application of the methods described.

Simulation study of relative permeability and the dynamic capillarity of waterflooding in tight oil reservoirs

Relative permeability (kr) and the capillary pressure (Pc) are the central key elements defining the multiphase fluids flow behavior in the porous media. However, the dynamic capillarity should consider the dynamic relative permeability and the dynamic capillary pressure while performing waterflooding process in extremely low permeable formations. In order to improve the oil production, the advanced horizontal well drilling along with multiple hydraulic fracturing is generally instigated to penetrate the unconventional resources. The aim of this study is to consider the dynamic capillarity in a commercial reservoir simulation, while utilizing the data gained from the dynamic and steady experiments of the relative permeability and the capillary pressure impacts during waterflooding process in the core plugs of unconventional tight oil reservoirs. The commercial reservoir simulation conducted sensitivity analyses using Computer Modeling Group simulator. The outcomes show that the well production of the reservoir is overestimated while implementing steady data for forecasting due to which the oil saturation decreases more equally and further rapidly. Additionally, the forecast of the well production estimated to breakthrough sooner. However, neglecting the dynamic capillarity causes a huge breakthrough of water influx. Therefore, the core objective of this study is to probe the consequences of taking into consideration the dynamic capillarity in ultra-low permeable formations while giving an alternative perspective to forecast the production of the hydraulically fractured unconventional tight oil reservoirs.

Monte Carlo simulation for uncertainty quantification in reservoir simulation: A convergence study

The current work illustrates the convergence properties of a Monte Carlo Simulation (MCS) used to quantify the geological uncertainty in reservoir simulation. We investigate the convergence behavior of MCS on 3D, 3-phase, highly heterogeneous reservoirs through real field data from a major oil and gas company. We generate 10,000 realizations of a geological model and run a Black-Oil flow simulation using a commercial reservoir simulator and a synchronous parallel implementation. The distributions of the moments and quantiles of the Net Present Value (NPV) are presented in the form of their Cumulative Density Functions (CDF). We also show the distributions of the break even time (BET) and the probability of breaking even in order to see the effect of considering quantities that are different in nature. We use log-plots to assess the convergence of the results, and verify that the convergence of the quantities of interest follows a squared-root law in the number of realizations used. We quantify the relative error made on various quantities and illustrate that the use of a small ensemble can yield errors of hundreds of percents, and that lowering the error to a given precision (e.g. below ten percent) can require thousands of realizations. For decision making and profitability assessments, using large sets of realizations is now feasible due to the availability of fast, distributed architectures and the parallel nature of MCS. Our results suggest that the improvement in the quality of the results is significant and well worth the extra effort. For optimization and sensitivity studies, running large ensembles is still intractable but yields sets of quantiles that can be used as a Reduced Order Model (ROM). Setting up a test case using this dataset is under consideration, and could provide an interesting integrated setup for comparisons of uncertainty quantification (UQ) methods.

Evaluation of Polymer Flooding in a Highly Stratified Heterogeneous Reservoir. A Field Case Study

Oil and gas companies are looking for proven hydrocarbon reserves from their mature drained reservoirs to extend the production and economic life of these fields. The chemical enhanced oil recovery (CEOR) is an attractive water-based EOR method for these mature fields. The polymer flooding (PF) is a widely applied process in reservoirs with low sweep efficiency after the water flooding (WF). The target Colombian field has one of the first polymer pilots in the region with positive results of oil recovery in “A” sands. Thus, the operator is interested in the expansion of PF for the same reservoir and even in deeper reservoir sands. This paper focuses in the evaluation of different scenarios of PF for the producer in layers A and B with a mechanistic simulation model, thus obtaining new recommendations for the recovery strategy in the field. A sector model was constructed from a full field model using a commercial reservoir simulator to the in-house chemical flooding reservoir simulator: UTCHEMRS. This sector model was also migrated to a second commercial simulator allowing a performance comparison for these three simulators. UTCHEMRS model results were compared with the commercial simulators through the history matching (HM) phase. The primary and waterflood history match was in agreement with the field data. Simulation results suggested that PF for the base case in “A” sands presented an incremental oil recovery of up to 12% additional to water flooding. Additionally, PF was extended to the lower layer “B” sand to investigate the potential of polymer injection. The PF injection in both reservoirs simultaneously loses sweep efficiency and decreases the oil recovery to about 3%. However, a hypothetical case of new infill producer wells with the objective of testing the individual reservoir performance has revealed that PF is having significant upside from B sands as well.

Numerical modeling of electromagnetic-based thermal recovery techniques combined with solvent injection

Electromagnetic heating (EMH) has been the focus of ever-increasing theoretical and experimental studies to examine if it can be used to heat geomaterials at the field scale. Bitumen recovery process using EMH technique assisted by solvent injection, such as that used in Enhanced Solvent Extraction Incorporating Electromagnetic Heating (ESEIEH®) pilot in currently being conducted in the Athabasca oil sands, utilizes radiofrequency waves to generate a moderate amount of heat in the reservoir. In conjunction with dilution effect of solvent the generated thermal energy reduces the viscosity of highly viscous bitumen leading to its mobilization.Electromagnetic (EM) wave propagation in a reservoir is a coupled multiphysics process that involves not only the heat transfer and fluid flow, but also EM field distribution. Currently, a coupled approach is followed by industry using a commercial reservoir simulator (CMG-STARS) and another commercial software (ANSYS-HFSS) or in-house electromagnetic wave solver with different gridding system where both are linked through a property mapping interface (CEMRS or AxHeat). The coupled scheme utilizes the interface to map parameters from two different gridding structures which causes an additional computational load on the simulation process and could be a source of error. This study presents an alternative integrated compositional numerical modeling approach to explore the EM heating phenomena pertinent to fluid flow and heat generation and transfer in oil sand reservoirs in the same grid structure. Energy balance and EM wave propagation are derived using Maxwell's equations in the frequency domain. Then, the newly developed numerical simulator is validated using benchmark problems and used to demonstrate the physics of EM-induced volumetric heat generation in multi-phase oil sand reservoirs. Finally, the developed numerical model is applied in a case study of propane injection along the antenna to achieve oil recovery and the results are compared with the analogous Steam Assisted Gravity Drainage (SAGD).

Modelling viscosity of liquid dropout near wellbore region in gas condensate reservoirs using modern numerical approaches

Liquid dropout occurs in gas condensate reservoirs below the dew point pressure around near wellbore region as a result of depletion from production of such reservoirs. Forecasting production as well as optimizing future recoveries of gas condensate reservoirs are highly desirable. This is not possible to achieve without accurate determination of liquid dropout viscosity (μc) below the dew point. The focus of research in past decades has been on the development of accurate viscosity prediction models below the dew point pressure to ensure accurate condensate production forecast. Gas condensate production forecast and optimization around wellbore region and condition are complicated due to unique gas condensate behaviour that violates thermodynamic laws.Current methods are based on correlation estimation, however the accuracy of these correlations are less than satisfactory, and root cause is due to the miscapturing of complex behaviour of gas condensate reservoir near the wellbore region. These motivated the consideration of modern numerical approaches such as the Least Square Support Vector Machine (LSSVM) and Artificial Neural Network (ANN) used in this paper. These methods are considered as more data behaviour oriented, with the capability of capturing the fluid complexity of gas condensate in such conditions.In this study viscosity of condensate phase near the wellbore region was modelled using machine learning techniques including ANN and LSSVM. For this purpose, over 300 viscosity data sets were collected from published literature and experimental studies worldwide. This databank includes API gravity, reservoir temperature, solution gas to oil ratio (Rs), specific gas gravity, fluid compositions and reservoir pressure.Six well known previously published viscosity correlations refined using least-square approach to match the experimental data. Qualitative and quantitative error analysis of developed LSSVM and ANN showed their performance superiority over refined literature correlations. The new proposed models can be embedded as an extra feature of commercial reservoir simulation packages for optimization and future recoveries of gas condensate reservoirs.

Simulations of In-Situ Upgrading Process: Interpretation of Laboratory Experiments and Study of Field-Scale Test

Summary In–situ upgrading (IU) is a promising method of improved viscous– and heavy–oil recovery. The IU process implies a reservoir heating up and exposure to a temperature higher than 300°C for a time period long enough to promote a series of chemical reactions. The pyrolysis reactions produce lighter oleic and gaseous components, while a solid residue remains underground. In this work, we developed a numerical model of IU using laboratory experience (kinetics measurements and core experiments) and validated the results by applying our model to an IU field–scale test published in the literature. Finally, we studied different operational conditions in a search for energy–efficient configurations. In this work, two types of IU experimental data are used from two vertical–tube experiments with Canadian bitumen cores (0.15 and 0.69 m). A general IU numerical model for the different experimental setups has been developed and compared with experimental data, using a commercial reservoir–simulator framework. This model is capable of representing the phase distribution of pseudocomponents, the thermal decomposition reactions of bitumen fractions, and the generation of gases and residue (solid) under thermal cracking conditions. Simulation results for the cores exposed to a temperature of 380°C and production pressure of 15 bar have shown that oil production (per pseudocomponent) and oil–sample quality were well–predicted by the model. Some differences in gas production and total solid residue were observed with respect to laboratory measurements. Computer–assisted history matching was performed using an uncertainty–analysis tool with the most–important model parameters. To better understand IU field–scale test results, the Shell Viking pilot (Peace River) was modeled and analyzed with the proposed IU model. The appropriate gridblock size was determined and the calculation time was reduced using the adaptive mesh–refinement (AMR) technique. The quality of products, the recovery efficiency, and the energy expenses obtained with our model were in good agreement with the field test results. In addition, the conversion results (upgraded oil, gas, and solid residue) from the experiments were compared with those obtained in the field test. Additional analysis was performed to identify energy–efficient configurations and to understand the role of some key variables (e.g., heating period and rate and the production pressure) in the global IU upgrading performance. We discuss these results, which illustrate and quantify the interplay between energy efficiency and productivity indicators.

Investigation of multiple hydraulic fractures evolution and well performance in lacustrine shale oil reservoirs considering stress heterogeneity

Giant Ordos basin, which locating in the northern China, consists of Palaeozoic lacustrine stacked sandstone and shale deposits with characteristics of low-permeability matrix rock and less-developed natural fractures (NFs). Recognizing the successful exploration of shale gas reservoirs in United States, Changqing oilfield uses advanced fracturing technology to create multiple straight hydraulic fractures (HFs) in shale oil zones with large differential horizontal stress. This paper firstly describes a hydraulic fracture propagation model (HFPM) that couples fracture deformation and fluid flow to simulate the growth of multiple HFs in the horizontal wellbore. We then integrated an embedded discrete fracture model (EDFM) and commercial reservoir simulator to analyze well performance based on the provided data from Changqing oilfield. We calibrated the fracture propagation model with reservoir performance data. Shale oil deposits in the Changqing oilfield have a relatively high brittleness index (BI) and large differential horizontal stress. This is the reason why reservoir with less-developed NFs system in the Ordos basin has less opportunity of generating complex fracture networks. Large-scale physical simulation experiments were conducted to validate the possibility of fracture networks generation. Our model considers the stress heterogeneity and offers the capability to address HF propagation in the presence of less-developed NFs and adjusts dimensions and spacing of NFs to achieve a history match with production data. This paper emphasizes the extensive application of an integrated model for lacustrine shale oil deposits. Considering features of Ordos basin, our model can provide a guideline for HF treatment design and optimization of well performance.

Feasibility study for carbon capture utilization and storage (CCUS) in the Danish North Sea

An innovative addition to the conventional oil and gas extraction by introducing enhanced oil recovery (EOR) techniques in the Danish North Sea is something which has been sought for long time. Combining the offshore EOR technology with the carbon capture and storage (CCS) is considered to be a possibly attractive solution as the associated benefit is twofold: (1) increased oil recovery through miscible displacement via CO2 pushing the hydrocarbons out of the reservoir and (2) the disposal of a greenhouse gas such as the CO2. A feasibility study for implementing a carbon capture, storage and utilization (CCUS) project is presented in the scope of this study, in a North Sea Chalk Field which is the oldest and one of the largest oilfields in the Danish sector, both for the technical and the economic merit, by means of using a commercial reservoir simulation package. The capital (CAPEX) and operational (OPEX) expenditures associated with such a long term development project is estimated. Results suggest that around 100 million barrels of additional oil reserves can be unlocked via CO2 injection, while 40 million tones of CO2 is being trapped in the reservoir.

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).

Modeling Dynamic Behaviors of Complex Fractures in Conventional Reservoir Simulators

Summary Field data have shown the decline of fracture conductivity during reservoir depletion. In addition, refracturing and infill drilling have recently gained much attention as efficient methods to enhance recovery in shale reservoirs. However, current approaches present difficulties in efficiently and accurately simulating such processes, especially for large-scale cases with complex hydraulic and natural fractures. In this study, a general numerical method compatible with existing simulators is developed to model dynamic behaviors of complex fractures. The method is an extension of an embedded discrete-fracture model (EDFM). With a new set of EDFM formulations, the nonneighboring connections (NNCs) in the EDFM are treated as regular connections in traditional simulators, and the NNC transmissibility factors are linked with gridblock permeabilities. Hence, manipulating block permeabilities in simulators can conveniently control the fluid flow through fractures. Complex dynamic behaviors of hydraulic fractures and natural fractures can be investigated using this method. The proposed methodology is implemented in a commercial reservoir simulator in a nonintrusive manner. We first present one synthetic case study in a shale-oil reservoir to verify the model accuracy and then combine the new model with field data to demonstrate its field applicability. Subsequently, four field-scale case studies with complex fractures in two and three dimensions are presented to illustrate the applicability of the method. These studies involve vertical- and horizontal-well refracturing in tight reservoirs, infill drilling, and fracture activation in a naturally fractured reservoir. The proposed approach is combined with empirical correlations and geomechanical criteria to model stress-dependent fracture conductivity and natural-fracture activation. It also shows convenience in dynamically adding new fractures or extending existing fractures during simulation. Results of these studies further confirm the significance of dynamic fracture behaviors and fracture complexity in the analysis and optimization of well performance.

Modeling oil saturation evolution in residual oil zones: Implications for CO2 EOR and sequestration

Abstract Residual oil zones (ROZs) are extensively developed in carbonate formations in the Permian Basin, West Texas. These ROZs have the potential both for economically-viable CO2 enhanced oil recovery (CO2-EOR) and for significant volumes of associated CO2 sequestration. The accepted model for ROZ formation is based on the hydrodynamic effects of tectonically-controlled increased water flow in aquifers at the base of oil fields. The nature of this process is modelled using a commercial reservoir simulator in this work. These simulations explore the effects of strength of aquifer flow, flow direction, and capillary pressure on the nature and distribution of oil saturations in ROZs. A special emphasis was on understanding the impact of heterogeneity of capillary pressures in ROZ reservoirs. These factors determine the thickness of ROZs, the magnitude of oil saturation, and the slope of water-oil contacts. Understanding the magnitude of oil saturation and how it varies within ROZs is important in determining reserves, and evaluating both EOR and sequestration potential. The geometry of ROZs are established slowly, especially for small regional water fluxes, however oil saturations achieve almost steady states in relatively short time scales. The simulated oil saturation profiles found in this study are in reasonable agreement with the measured profile published for the San Andres Seminole Unit's ROZ. The results support the plausibility of the hydrodynamic model, but do not rule out other models for the origin of ROZs.

Dynamic coupling of analytical linear flow solution and numerical fracture model for simulating early-time flowback of fractured tight oil wells (planar fracture and complex fracture network)

Abstract Quantitative analysis of flowback data provides an early opportunity to interpret hydraulic fracture properties of fractured wells in unconventional reservoirs. In recent years, a number of studies have been dedicated to the analysis of single-phase flow data prior to hydrocarbon breakthrough and multi-phase flow data after hydrocarbon breakthrough during flowback. This study provides a new model to simulate flowback of fractured tight oil wells with planar fractures or a complex fracture network. In the model, fractures are discretized into a number of segments and the fully numerical method is used to model fracture flow. It is assumed that the pressure interference in the matrix caused by these fracture segments can be accounted using a no-flow boundary condition and that the drainage volumes of fracture segments form isolated regions to allow for linear flow from the matrix to each segment. To model this behavior, matrix flow is simulated using the analytical transient linear flow solution which is dynamically coupled with the fracture flow model by imposing continuity of pressure and flux on the fracture surfaces. The model is simple, but rigorous enough to take into account the important physics of the fracture system, including arbitrary fracture geometries and fracture conductivity distributions. The pressure and saturation gradients of each phase in fractures are accounted for. The ease of model setup and improved computational performance makes it convenient for practical application. The new model is verified with a commercial reservoir simulator. Investigation of the assumption of no-flow boundary condition for the pressure interference in the matrix caused by the fracture segments reveals that the assumption becomes poorer with increasing matrix permeability and decreasing fracture permeability. Flow regime analysis demonstrates that, for planar fractures, water exhibits an early transient linear flow period (first linear flow in the fracture) followed by boundary-dominated flow (first boundary-dominated flow in the fracture) prior to hydrocarbon breakthrough, after which a second transient linear flow in the fracture develops. The final flow period is boundary-dominated flow (second boundary-dominated flow in the fracture), indicating the end time of the flowback period. At this stage, oil will be the dominant phase in the fracture, and will exhibit transient linear flow in the matrix, which is the first flow-regime typically observed during the long-term production period. The case of a complex fracture network exhibits a similar sequence of flow regimes. The second transient linear flow for this case has not been discussed in the literature. The development of this flow period may be caused by the maintenance of fracture pressure, and decrease of water relative permeability due to hydrocarbon contribution from the matrix. A field example from western Canada exhibits the flow regimes noted above and is history-matched successfully using the new model. The results demonstrate the practical application of the new model for deriving reservoir/fracture properties from flowback data and for forecasting.

A practical and efficient iterative history matching workflow for shale gas well coupling multiple objective functions, multiple proxy-based MCMC and EDFM

A manual history matching is a time-consuming process and too risky for an investment decision because it cannot capture the uncertainty of subsurface information and production forecast. Therefore, Assisted History Matching (AHM) was adopted to obtain multiple reservoir realizations for the probabilistic production forecast. Markov Chain Monte Carlo (MCMC) is one of the AHM in the Bayesian statistics that have benefits of quantifying uncertainty without bias, not being trapped in any local minima. However, the AHM in shale reservoirs has not been widely studied due to the unconventional shale's complexities such as fracture modeling. The continuum approach cannot accurately model the connection between hydraulic fractures and matrix blocks in shale reservoirs. While, Discrete Fracture Modeling (DFM) by using local grid refinement can overcome the issue of the continuum approach, there is still a limitation of computational efficiency. Therefore, Embedded Discrete Fracture Modeling (EDFM) has been developed to combine the benefits of dual-continuum model and DFM. While DFM is an intrusive technique to model fractures, EDFM can account the mass transfer between matrix cells and fractures without being intrusive to the model. Besides, EDFM provides more efficient computational time than DFM. In this study, we proposed a practical and efficient iterative AHM workflow by integrating the benefits of using multiple objective functions, multiple proxy-based MCMC algorithm, EDFM and commercial reservoir simulator. Then, we demonstrated an application of the proposed workflow to a real shale gas well by matching both gas and water rates. The field application shows that the proposed AHM workflow efficiently finds the history matching solutions of 64 out of 300 simulation runs (21%) with a total computational time of one day. Furthermore, the workflow captures the uncertainty of reservoir and fracture parameters in terms of posterior distribution. Also, we performed the probabilistic production forecast and obtained P10-50-90 estimated ultimate recovery of gas production.