Heterogeneous Reservoir Models Research Articles

The Effect of Surfactant Polymer Concentration on the Distribution of Oil Drainage Area in Heterogeneous Reservoir


 
 
 The use of chemical injection has been widely used in the oil field on a large scale. One of the enhanced oil recovery (EOR) methods to increase production from old oil fields is through polymer surfactant injection, which functions to reduce interfacial tension and water-oil mobility ratio. This study focuses on developing a simulation model for chemical injection of polymer surfactant reservoirs by hypothetically making heterogeneous reservoir models in each layer with dimensions of 10x10x4. It consists of one a vertical well which is producer well located at the top of the left corner and one an injection well which is located at the bottom of right corner. This study shows a comparison between surfactant injection, polymer injection and SP injection using the same surfactant and polymer concentration with a concentration of 1000 ppm with 0.3 PV. Oil recovery in polymer injection turned out to be quite high compared to other chemical injections. In polymer injection, the oil recovery was 4.17%. Meanwhile, surfactant injection and SP injection increased by 0.59% and 0.61, respectively.
 
 

Hydrogen underground storage efficiency in a heterogeneous sandstone reservoir

Underground hydrogen storage has been recognized as a key technology for storing enormous amounts of hydrogen, thus aiding in the industrial-scale application of a hydrogen economy. However, underground hydrogen storage is only poorly understood, which leads to high project risk. This research thus examined the effect of caprock availability and hydrogen injection rate on hydrogen recovery factor and hydrogen leakage rate to address some fundamental questions related to underground hydrogen storage. A three dimensional heterogeneous reservoir model was developed, and the impact of caprock and hydrogen injected rate on hydrogen underground storage efficiency were analysed with the model. The results indicate that both caprock and injection rate have an important impact on hydrogen leakage, and the quantities of trapped and recovered hydrogen. It is concluded that higher injection rate increases H2 leakage when caprocks are absent. In addition, lower injection rates and caprock availability increases the amount of recovered hydrogen. This work therefore provided fundamental information regarding underground hydrogen storage project assessment, and supports the decarbonisation of the energy supply chain. Cited as: Mahdi, D.S., Al-Khdheeawi, E.A., Yuan, Y., Zhang, Y., Iglauer, S. Hydrogen underground storage efficiency in a heterogeneous sandstone reservoir. Advances in Geo-Energy Research, 2021, 5(4): 437-443, doi: 10.46690/ager.2021.04.08

Research and practice on oil displacement law of different well pattern in offshore oilfield

ABSTRACT BZ oilfield is the first oilfield successfully developed with large-scale horizontal well pattern in Bohai sea. In order to study the well pattern suitable for offshore fluvial sedimentary oilfield, based on the characteristics of offshore oilfield, such as large well spacing, few wells and complex well pattern, the authors designed a set of heterogeneous reservoir models under the guidance of similarity theory. The different well patterns, different displacement rates and different heterogeneity were simulated. The different displacement effects were studied by measuring the electrical characteristics, injection volume, oil production and water production of the model. It is found that the horizontal well pattern is used for low-speed development, the water breakthrough of oil wells is slow in the relatively homogeneous reservoir. In the heterogeneous high permeability reservoir(Jk = 4), the oilfield adopts low-speed development by using staggered well pattern, the water breakthrough of the oil well is slow, and the EOR is large. In the heterogeneous high permeability reservoir(Jk = 6), the oilfield adopts the combined well pattern for low-speed development, the EOR is large. This study deepens the understanding of the development law of horizontal well pattern and the distribution law of remaining oil, and provides a basis for oilfield development and adjustment.

Effects of the spatial heterogeneity in reservoir parameters on the heat extraction performance forecast based on a 3D thermo-hydro-mechanical coupled model: A case study at the Zhacang geothermal field in the Guide basin, northeastern Tibetan plateau

Geothermal extraction assessment is of considerable importance for the reservoir operation management. Based on the data of the high-temperature reservoir of the Zhacang geothermal field in the Guide basin, northeastern Tibetan plateau, this study explores the effects of the heterogeneity in the reservoir parameters on the assessment of the reservoir extraction ability via a 3D thermo-hydro-mechanical (THM) fully coupled model and compares the prediction discrepancies between the heterogeneous reservoir models with the constant and stress-dependent permeability. Modeling results show that the variance of the specific heat capacity has a negligible impact on the reservoir performance, whereas the changes in the correlation length can result to relatively apparent difference in both the outlet temperature and effective electric power rate. Compared with the specific heat capacity, the spatial heterogeneity in permeability has greater significant effect on the reservoir performance, which may be related to the occurrence of the preferential flow with the increasing variance and correlation length of the permeability. The prediction difference of the reservoir performance emphasizes the great importance of the stress-dependent reservoir permeability in a THM coupled reservoir numerical model to accurately assess the extraction ability of a geothermal reservoir concerned.

LABORATORY TESTING OF ACID OIL-DISPLACING COMPOSITION TO ENHANCE OIL RECOVERY

The article presents the results of laboratory tests of an acid oil-displacing composition based on a surfactant developed at the Institute of Petroleum Chemistry of the Siberian Branch of the Russian Academy of Sciences to enhance oil recovery. The experiments were carried out on a installation to study the filtration characteristics of heterogeneous reservoir models. It was found that the use of the composition leads to a significant increase in the oil displacement coefficient.

Application of Deep Learning for Characterization of CO2 Leakage Based on Above Zone Monitoring Interval (AZMI) Pressure Data

CCS is an important component of worldwide strategy to tackle climate change. The CO2 will be captured and stored in subsurface reservoirs for a near future to help reducing the greenhouse gas emissions to the atmosphere. To ensure that the CO2 is retained within the reservoir, proper monitoring of CCS sites is necessary. Such monitoring is important for regulators, public perception, the environment and safety. Here we present a framework that uses deep learning methodology, based on reservoir simulations and information from Above Zone Monitoring Interval (AZMI) wells, to detect, localize and quantify potential CCS-leakages trough a Multi-Task-Learning (MTL) framework. To simulate subsurface CO2 movement and its effects on the pressure in the AZMI, we use a high fidelity reservoir model. In particular, in this study we use a heterogeneous 2D spatial reservoir model to simulate different leak fluxes and locations. The work presented here is a short summary of the work presented in the paper A Variational Auto-Encoder for Reservoir Monitoring available on arxiv.

Infill well placement optimization in two-dimensional heterogeneous reservoirs under waterflooding using upscaling wavelet transform

Optimal location of infill wells is very important in the development strategies of any reservoir. It is even one of the most challenging tasks for waterflooding projects in heterogeneous reservoirs. The new infill wells have to be located based on optimum reservoir management practice taking into considerations several reservoir parameters. Usually, there are several potential locations of the new infill wells. Selecting and evaluating of all the possible candidate wells using fine scale models is neither computationally straightforward nor time efficient for geological models consisting of millions of grid cells. This paper presents a practical and cost effective method that minimizes the number of simulation runs and maximizes oil recovery by optimizing infill well location. The proposed method consists of upscaling the geological reservoir model from fine scale into a coarser scale while preserving the important characteristics of the fine scale model. The proposed approach uses wavelet transform as an upscaling technique. To illustrate its feasibility and effectiveness, the proposed method is applied to three synthetic cases of two-dimensional inverted five-spot heterogeneous reservoir models. Static Dykstra-Parsons coefficient of permeability variation is utilized to compute the degree of heterogeneity of each of the three models. Permeability distribution values are used to represent and construct the reservoir models. The permeability models are upscaled from the original fine grid model to coarser scale levels. A computationally efficient and extremely time saving three-dimensional streamline simulator is used to implement the proposed approach. This paper shows that the results obtained from the upscaled models are in excellent agreement with the fine scale models. More importantly, the results also illustrate that the proposed method extensively reduces the number of simulation runs required to find the optimum well location between 75% and 93% depending on the reservoir heterogeneity. The methodology presented in this paper provides a straightforward road map for upscaling several heterogeneous reservoir models using wavelet transform approach to determine the optimum infill well location.

A Novel Streamline-Based Objective Function for Well Placement Optimization in Waterfloods

AbstractIn reservoir development plans, well placement optimization is usually performed to better sweep oil and reduce the amount of trapped oil inside reservoirs. Long-term optimization of well placement requires multiple times simulation of reservoirs which makes these problems cumbersome, especially when a large number of decision variables exist. Cumulative oil production (COP) or net present value (NPV) functions are commonly used as the objective function of optimal enhance oil recovery projects. Use of these functions requires a full-time reservoir simulation and their convergence could be difficult with the chance to be trapped in local optimum solutions. In this study, the novel proportionally distributed streamlines (PDSLs) target function is proposed that can be minimized to reach the optimal well placement. PDSL can be estimated even without full-time reservoir simulation. PDSL tries to direct the appropriate number of streamlines toward the regions with larger amount of oil in the shortest time and hence can improve oil recovery. Particle swarm optimization (PSO) method linked to an in-house streamline-based reservoir simulator is implemented to optimize well placement of water-flooding problems in a two-dimensional heterogeneous reservoir model.

Determining Reservoir Model Based on Welltest Analysis for Production Forecasting

Welltest analysis is basically an analysis of fluid flow behavior in porous rocks, both in oil reservoirs and gas reservoirs that aim to obtain reservoir characteristics. Welltest analysis is not only limited to get reservoir characteristics and well productivity, but to determine the reservoir and boundary models that are close to the real situation. Until now, to determine the reservoir and boundary models are usually assisted by using a simulator, which is called modeling. However, to determine the reservoir and boundary models has its own challenges, because there are no specific methods or equations that can know for certain the model of a reservoir. Choosing the right type of model also requires some consideration, such as seismic data, geological data, log data, and information provided from other wells drilled into the same formation. From the determination of this model can be used as supporting information for a reservoir, which is then used to be a predictive model and renewal of its geological model, as well as allowing engineers to simulate production estimates. In this paper, we will present an analysis of determining the right reservoir model based on welltest analysis, which selected model is used to simulate production estimates. Case example is taken from an oil well named "HBR-05", which has been carried out a pressure build-up test on one productive layer that until now has not been produced or closed since the well testing was carried out. From the determination of the reservoir model based on welltest analysis, obtained heterogeneous anisotropic reservoir model and rectangular-no flow boundary model. The selected reservoir model is used to simulate the estimated production of the "HBR-05" well if it is produced from this layer. Production forecasting results for 48 months (4 years) the "HBR-05" well, the cumulative production is 107.8 Mbbl. In addition, the results of forecasting show that the reservoir pressure is already below the bubble point pressure in the 22nd month of forecasting.

Physical Modeling of Oil Displacement in the Multifunctional Surfactant-based Chemical Composition

The paper presents the test results of a new multifunctional chemical composition based on surfactants in the process of displacement of heavy high-viscosity oil. Physical modeling of the oil displacement process was carried out in a laboratory setup which allows modeling heterogeneity of the oil-saturated formation. As a result of experiments it was found that the use of a multifunctional chemical oil-displacing composition based on a surfactant leads not only to a change in the filtration flows within the heterogeneous reservoir model but also to an increase in the oil displacement coefficient.

Laboratory study of the oil displacing ability of a chemical acid oil-displacing composition

The paper presents the results of laboratory experiments to study the filtration characteristics of models of a heterogeneous reservoir of a Permian-carbon deposit in the Usinsk mine and the oil-displacing ability of a chemical acid oil displacing composition (AODC). The experiments were carried out on a setup for studying the filtration characteristics of heterogeneous reservoir models. The study found that the use of AODC will lead to a significant increase in the coefficient of oil-displacement.

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.

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 new well pattern of cluster-layout for deep geothermal reservoirs: Case study from the Dezhou geothermal field, China

A well pattern of cluster-layout is proposed to reduce the risk of thermal breakthrough at production wells, as well as to improve the convenience of surface management. Its performance in the heterogeneous porous geothermal reservoir models based on the Dezhou geothermal field, China is systematically examined using the coupled thermal-hydraulic numerical modeling. In order to compare the performance of different well patterns, three assessment parameters including thermal breakthrough time and water table of production wells and recovery efficiency of geothermal energy in the licensed region are defined. The results showed that the well pattern of cluster-layout has the higher recovery efficiency of geothermal energy compared with other well patterns of tramrail-layout and checkers-board-layout for most simulations. Effects of anisotropy and correlation length of reservoir permeability on the performance of different well patterns are examined. Flow channeling appears along the direction of high permeability, and thus the injection-production direction should avoid to be parallel with that direction. With increasing correlation length, the possibility of flow channels appearing in the licensed region increases and the fate of production wells declines. When the difference of correlation lengths between different directions increases, the induced anisotropy of reservoir permeability increases but the recovery efficiency decreases.

Integration of Pressure Transient Data into Reservoir Models Using the Fast Marching Method

SummaryCalibration of reservoir model properties by integration of well-test data remains an important research topic. Well-test data have been recognized as an effective tool to describe transient flow behavior in petroleum reservoirs. It is also closely related to the drainage volume of the well and the pressure-front propagation in the subsurface. Traditional analytic means of estimating reservoir permeability relies on an interpretation of the diagnostic plot of the well pressure and production data, which usually leads to a bulk average estimation of the reservoir permeability. When more detailed characterization is needed, a forward model that is sensitive to the reservoir heterogeneity needs to be established, and a numerical inversion technique is required.We use the concept of the diffusive time of flight (DTOF) to formulate an asymptotic solution of the diffusivity equation that describes transient flow behavior in heterogeneous petroleum reservoirs. The DTOF is obtained from the solution of the Eikonal equation using the fast marching method (FMM). It can be used as a spatial coordinate that reduces the 3D diffusivity equation to an equivalent 1D formulation. We investigate the drainage-volume evolution as a function of time in terms of the DTOF. The drainage volume might be directly related to the well-test derivative, which can be used in an inversion calculation to calibrate reservoir model parameters.The analytic sensitivity coefficients of the well-test derivative with respect to reservoir permeability are derived and incorporated into an objective function to perform model calibration. The key to formulating the sensitivity coefficients is to use the functional derivative of the Eikonal equation to derive the analytic sensitivity of the DTOF to reservoir permeability. Its solution is implemented by tracking the characteristic trajectory of the local Eikonal solver within the FMM. The major advantage of calculating the sensitivity coefficients using the FMM is its significant computational efficiency during the iterative inversion process.This inverse-modeling approach is tested on a 2D synthetic heterogeneous reservoir model and then applied to the 3D Brugge Field, where a single well with constant flow rate is simulated. The well-test derivative is shown to be inversely proportional to the drainage volume and is treated as the objective function for inversion. With an additional constraint to honor the prior model, our inverse-modeling approach will adjust the reservoir model to obtain permeability as a function of distance from the well within the drainage volume. It provides a modification of reservoir permeability both within and beyond the depth of investigation (DOI).

An accelerated gradient algorithm for well control optimization

The simulation-assisted optimization of injection and production control settings can be considered as a way for improved management of hydrocarbon reservoirs. Although well control optimization problems are expected to have a relatively smooth landscape, they suffer from having a high number of dimensions. As a result, gradient-based algorithms are typically the preferred choice over derivative-free algorithms. Since the calculation of the exact gradient is not always feasible, recent studies into gradient-based algorithms have focused on ameliorating the accuracy of gradient approximation methods. Less attention has been paid to the framework in which these gradient approximations are utilized. The most common framework is the steepest descent with a backtracking line-search. This study aims to provide an improved framework using an adaptive moment estimation technique. The proposed method is compared to the steepest descent framework in two case studies of increasing complexity. In both frameworks, the gradient is approximated by the simultaneous perturbation stochastic approximation. The first case study is a two-dimensional heterogeneous reservoir model produced under water-flooding. The second case study investigates the injection and production settings of the three-dimensional benchmark case, the Brugge model, whilst incorporating geological uncertainty. The proposed framework showed improvements of up to 91% in convergence speeds and up to 5% in optimal value for the first case study. In addition, the proposed framework showed an improvement of up to 81% in convergence speeds and up to 2% in optimal value for the second case study over the steepest descent framework. For both case studies, the effect of the gradient approximation accuracy (perturbation number) was also investigated. The results indicated that the proposed algorithm is less sensitive to the gradient approximation accuracy than the steepest descent framework. In addition, this study investigated the effect of two popular bound constraint handling techniques in both frameworks. The results indicated that the projection method outperforms the logarithmic transformation method when applied to production optimization problem.

Development of an efficient hybrid GA-PSO approach applicable for well placement optimization

When it comes to the economic efficiency of oil and gas field development, finding the optimum well locations that augment an economical cost function like net present value (NPV) is of paramount importance. Well location optimization has long been a challenging problem due to the heterogeneous nature of hydrocarbon reservoirs, economic criteria, and technical uncertainties. These complexities lead to an enormous number of possible solutions that must be evaluated using an evaluation function (e.g. a simulator). This makes it necessary to develop a powerful optimization algorithm into which a fast function evaluation tool is incorporated. The present study describes the application of a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) into a hybrid GA-PSO algorithm that is implemented in a streamline simulator to determine optimal locations for production and injection wells across heterogeneous reservoir models. Performance of the hybrid GA-PSO algorithm is then compared to that of the PSO and the GA separately. The results confirm that compared to conventional methods, the recommended method provides a fast and well-defined approach for production optimization complications. Cited as : Yazdanpanah, A., Rezaei, A., Mahdiyar, H., Kalantariasl, A. Development of an efficient hybrid GA-PSO approach applicable for well. Advances in Geo-Energy Research, 2019, 3(4): 365-374, doi: 10.26804/ager.2019.04.03

Characterization of three-dimensional channel reservoirs using ensemble Kalman filter assisted by principal component analysis

sEnsemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemble size, and then, EnKF is utilized to predict production performances stochastically. In the model selection scheme, representative models are chosen by using principal component analysis (PCA) and clustering analysis. The dimension of initial models is reduced using PCA, and the reduced models are grouped by clustering. Then, we choose and simulate representative models from the cluster groups to compare errors of production predictions with historical observation data. One representative model with the minimum error is considered as the best model, and we use the ensemble members near the best model in the cluster plane for applying EnKF. We demonstrate the proposed scheme for two 3D models that EnKF provides reliable assimilation results with much reduced computation time.

Experimental investigation on block and transport characteristics of foam in porous media for enhanced oil recovery processes

Foam microscopic block characteristics were investigated in a 2D-visualization flow test and big 3D core foam flooding experiment. The foam macroscopic block characteristics and transport mechanism in porous media are investigated by measuring the production performance and pressure distribution in a positive rhythm heterogeneous reservoir model. Blockage of the throat by bubbles happens with single large bubble blocking or small bubble bridging and composite plugging with multiple bubbles. The distribution of the high-pressure gradient zone at a different time reflects the transport characteristic of the foam front and the oil bank formed during foam flooding. The selective blocking feature can effectively increase flow resistance and pressure, and promote the flow direction from the high permeability layer to the middle and low permeability layer, resulting in the expansion of swept volume. When 0.8 PV foam with the quality of 66.7% is injected into the big 3D core in the tertiary model, an incremental oil recovery of 21.19% OOIP is observed. The inlet pressure rises sharply from 40 kPa at waterflooding to 156.60kPa after foaming flooding, increasing approximately by 4 times. Foam can not only improve the displacement efficiency in the high permeability layers but also improve the displacement and expanding efficiency in the mid and low permeability layers. After the foam flooding, the incremental oil recovery rate in the high, media and low permeability layer is about 18.33%, 20.03%, and 23.23% OOIP respectively, indicating the influence of heterogeneity and effectiveness of foaming flooding. The primary way to enhance oil recovery in heterogeneous reservoirs is to increase oil displacement efficiency based on expanding the sweeping volume. In addition, the results of 3D foam EOR experiment provide an experimental basis for numerical simulation and play an important role in understanding and the application of the foam-EOR method in the heterogeneous reservoirs.

Coupled thermo-hydro-mechanical modeling on well pairs in heterogeneous porous geothermal reservoirs

Production water reinjection plays an important role in geothermal reservoir management. Heterogeneous porous geothermal reservoir models considering the spatial distributions of permeability and rock heat capacity are generated. In total, 200 realizations with different permeability fields were considered. A fully coupled modeling of reservoir deformation, fluid flow, and heat transport was performed on each realization using the finite element method. The results showed that heterogeneity induced channeling appears in the geothermal reservoirs with increasing heterogeneity of permeability, through which the injected production can quickly reach the production well. This causes the shorter thermal breakthrough time at production wells, and the lower reservoir surface settlement. In geothermal reservoir models considering the correlation between heat capacity and permeability, the thermal breakthrough curves and the reservoir surface settlement were the same as the reservoir models of the constant heat capacity. With increasing correlation length, the possibility of flow channels appearing in well pair system increases, causing a short average thermal breakthrough time and a lower surface settlement around the injection well. The existence of preferential flow channels between injection and production wells indicates that an equivalent flow channel model can be approximately simulate geothermal doublets in heterogeneous reservoirs.