Enhanced history matching process by incorporation of saturation logs as model selection criteria

This paper proposes a methodology for an alternative history matching process enhanced by the incorporation of a simplified binary interpretation of reservoir saturation logs(RST) as objective function. Incorporating fluids saturation logs during the history matching phase unlocks the possibility to adjust or select models that better represent the near wellbore waterfront movement, which is particularly important for uncertainty mitigation during future well interference assessments in water driven reservoirs. For the purposes of this study, a semi-synthetic open-source reservoir model was used as base case to evaluate the proposed methodology. The reservoir model represents a water driven, highly heterogenous sandstone reservoir from Namorado field in Brazil. To effectively compare the proposed methodology against the conventional methods, a commercial reservoir simulator was used in combination with a state-of-the-art benchmarking workflow based on the Big LoopTMapproach. A well-known group of binary metrics were evaluated to be used as the objective function, and the Matthew correlation coefficient(MCC) has been proved to offer the best results when using binary data from water saturation logs. History matching results obtained with the proposed methodology allowed the selection of a more reliable group of reservoir models,especially for cases with high heterogeneity. The methodology also offers additional information and understanding of sweep behaviour behind the well casing at specific production zones, thus revealing full model potential to define new wells and reservoir development opportunities.

Iterative learning-based many-objective history matching using deep neural network with stacked autoencoder

This paper presents an innovative data-integration that uses an iterative-learning method,a deep neural network(DNN)coupled with a stacked autoencoder(SAE)to solve issues encountered with many-objective history matching.The proposed method consists of a DNN-based inverse model with SAE-encoded static data and iterative updates of supervised-learning data are based on distance-based clustering schemes.DNN functions as an inverse model and results in encoded flattened data,while SAE,as a pre-trained neural network,successfully reduces dimensionality and reliably reconstructs geomodels.The iterative-learning method can improve the training data for DNN by showing the error reduction achieved with each iteration step.The proposed workflow shows the small mean absolute percentage error below 4%for all objective functions,while a typical multi-objective evolutionary algorithm fails to significantly reduce the initial population uncertainty.Iterative learning-based manyobjective history matching estimates the trends in water cuts that are not reliably included in dynamicdata matching.This confirms the proposed workflow constructs more plausible geo-models.The workflow would be a reliable alternative to overcome the less-convergent Pareto-based multi-objective evolutionary algorithm in the presence of geological uncertainty and varying objective functions.

Multi-source information fused generative adversarial network model and data assimilation based history matching for reservoir with complex geologies

For reservoirs with complex non-Gaussian geological characteristics,such as carbonate reservoirs or reservoirs with sedimentary facies distribution,it is difficult to implement history matching directly,especially for the ensemble-based data assimilation methods.In this paper,we propose a multi-source information fused generative adversarial network(MSIGAN)model,which is used for parameterization of the complex geologies.In MSIGAN,various information such as facies distribution,microseismic,and inter-well connectivity,can be integrated to learn the geological features.And two major generative models in deep learning,variational autoencoder(VAE)and generative adversarial network(GAN)are combined in our model.Then the proposed MSIGAN model is integrated into the ensemble smoother with multiple data assimilation(ESMDA)method to conduct history matching.We tested the proposed method on two reservoir models with fluvial facies.The experimental results show that the proposed MSIGAN model can effectively learn the complex geological features,which can promote the accuracy of history matching.

Geophysical and Production Data History Matching Based on Ensemble Smoother with Multiple Data Assimilation

The Ensemble Kalman Filter(EnKF),as the most popular sequential data assimilation algorithm for history matching,has the intrinsic problem of high computational cost and the potential inconsistency of state variables updated at each loop of data assimilation and its corresponding reservoir simulated result.This problem forbids the reservoir engineers to make the best use of the 4D seismic data,which provides valuable information about the fluid change inside the reservoir.Moreover,only matching the production data in the past is not enough to accurately forecast the future,and the development plan based on the false forecast is very likely to be suboptimal.To solve this problem,we developed a workflow for geophysical and production data history matching by modifying ensemble smoother with multiple data assimilation(ESMDA).In this work,we derived the mathematical expressions of ESMDA and discussed its scope of applications.The geophysical data we used is P-wave impedance,which is typically included in a basic seismic interpretation,and it directly reflects the saturation change in the reservoir.Full resolution of the seismic data is not necessary,we subsampled the P-wave impedance data to further reduce the computational cost.With our case studies on a benchmark synthetic reservoir model,we also showed the supremacy of matching both geophysical and production data,than the traditional reservoir history matching merely on the production data:the overall percentage error of the observed data is halved,and the variances of the updated forecasts are reduced by two orders of the magnitude.

An Improved Parameter Dimensionality Reduction Approach Based on a Fast Marching Method for Automatic History Matching

History matching is a critical step in reservoir numerical simulation algorithms.It is typically hindered by difficulties associated with the high-dimensionality of the problem and the gradient calculation approach.Here,a multi-step solving method is proposed by which,first,a Fast marching method(FMM)is used to calculate the pressure propagation time and determine the single-well sensitive area.Second,a mathematical model for history matching is implemented using a Bayesian framework.Third,an effective decomposition strategy is adopted for parameter dimensionality reduction.Finally,a localization matrix is constructed based on the single-well sensitive area data to modify the gradient of the objective function.This method has been verified through a water drive conceptual example and a real field case.The results have shown that the proposed method can generate more accurate gradient information and predictions compared to the traditional analytical gradient methods and other gradient-free algorithms.

Sensitivity-based upscaling for history matching of reservoir models

Simulation of reservoir flow processes at the finest scale is computationally expensive and in some cases impractical.Consequently,upscaling of several fine-scale grid blocks into fewer coarse-scale grids has become an integral part of reservoir simulation for most reservoirs.This is because as the number of grid blocks increases,the number of flow equations increases and this increases,in large proportion,the time required for solving flow problems.Although we can adopt parallel computation to share the load,a large number of grid blocks still pose significant computational challenges.Thus,upscaling acts as a bridge between the reservoir scale and the simulation scale.However as the upscaling ratio is increased,the accuracy of the numerical simulation is reduced;hence,there is a need to keep a balance between the two.In this work,we present a sensitivity-based upscaling technique that is applicable during history matching.This method involves partial homogenization of the reservoir model based on the model reduction pattern obtained from analysis of the sensitivity matrix.The technique is based on wavelet transformation and reduction of the data and model spaces as presented in the 2Dwp-wk approach.In the 2Dwp-wk approach,a set of wavelets of measured data is first selected and then a reduced model space composed of important wavelets is gradually built during the first few iterations of nonlinear regression.The building of the reduced model space is done by thresholding the full wavelet sensitivity matrix.The pattern of permeability distribution in the reservoir resulting from the thresholding of the full wavelet sensitivity matrix is used to determine the neighboring grids that are upscaled.In essence,neighboring grid blocks having the same permeability values due to model space reduction are combined into a single grid block in the simulation model,thus integrating upscaling with wavelet multiscale inverse modeling.We apply the method to estimate the parameters of two synthetic reservoirs.The history matching results obtained using this sensitivity-based upscaling are in very close agreement with the match provided by fine-scale inverse analysis.The reliability of the technique is evaluated using various scenarios and almost all the cases considered have shown very good results.The technique speeds up the history matching process without seriously compromising the accuracy of the estimates.

A multiobjective dominance and decomposition algorithm for reservoir model history matching

Reservoir model history matching is a multiobjective optimization problem.It involves the adjustment of relevant reservoir model input parameters,to minimize the mismatch between simulated and observed reservoir responses and to obtain a diverse set of geologically plausible reservoir simulation models.Typically,single objective optimization methods are adopted during history matching.This requires weighted sum scalarization.However,scalarization biases the optimization search,limiting the diversity of the recovered solutions.In this work,a computer assisted history matching procedure based on transform parameterization and a multiobjective evolutionary algorithm with dominance and decomposition(MOEA/DD)is proposed.In the procedure,history matching is treated as a multiobjective optimization problem,parameterized in terms of a small number of kernel principal component analysis(KPCA)variables.KPCA provides efficient parameterization of the reservoir model input property fields.Concurrently,MOEA/DD provides robust and unbiased optimization over multiple objectives.The effectiveness of the proposed procedure is demonstrated with the UNISIM-I-H history matching benchmark problem.

Research on Quantitative Identification of Three-Dimensional Connectivity of Fractured-Vuggy Reservoirs

The fractured-vuggy carbonate oil resources in the western basin of China are extremely rich.The connectivity of carbonate reservoirs is complex,and there is still a lack of clear understanding of the development and topological structure of the pore space in fractured-vuggy reservoirs.Thus,effective prediction of fractured-vuggy reservoirs is difficult.In view of this,this work employs adaptive point cloud technology to reproduce the shape and capture the characteristics of a fractured-vuggy reservoir.To identify the complex connectivity among pores,fractures,and vugs,a simplified one-dimensional connectivity model is established by using the meshless connection element method(CEM).Considering that different types of connection units have different flow characteristics,a sequential coupling calculation method that can efficiently calculate reservoir pressure and saturation is developed.By automatic history matching,the dynamic production data is fitted in real-time,and the characteristic parameters of the connection unit are inverted.Simulation results show that the three-dimensional connectivity model of the fractured-vuggy reservoir built in this work is as close as 90%of the fine grid model,while the dynamic simulation efficiency is much higher with good accuracy.

Iterative static modeling of channelized reservoirs using history-matched facies probability data and rejection of training image

Most inverse reservoir modeling techniques require many forward simulations, and the posterior models cannot preserve geological features of prior models. This study proposes an iterative static modeling approach that utilizes dynamic data for rejecting an unsuitable training image(TI) among a set of TI candidates and for synthesizing history-matched pseudo-soft data. The proposed method is applied to two cases of channelized reservoirs, which have uncertainty in channel geometry such as direction, amplitude, and width. Distance-based clustering is applied to the initial models in total to select the qualified models efficiently. The mean of the qualified models is employed as a history-matched facies probability map in the next iteration of static models. Also, the most plausible TI is determined among TI candidates by rejecting other TIs during the iteration. The posterior models of the proposed method outperform updated models of ensemble Kalman filter(EnKF) and ensemble smoother(ES) because they describe the true facies connectivity with bimodal distribution and predict oil and water production with a reasonable range of uncertainty. In terms of simulation time, it requires 30 times of forward simulation in history matching, while the EnKF and ES need 9000 times and 200 times, respectively.

An improved coupled flow-geomechanical model for cyclic steam stimulation

Considering the pore deformation and permeability changes during dilation-recompaction in cyclic steam stimulation(CSS),an existing geomechanical model is improved and thermo-mechanically coupled with the flow equations to form a coupled flow-geomechanical model.The impacts of dilation-recompaction parameters can be quantified through sensitivity analysis and uncertainty assessment utilizing the synergy between Latin hypercube designs and response surface methodology.The improved coupled flow-geomechanical model allows a more reasonable history-matching of steam injection pressure and volume and oil/water production volume.In both the linear and quadratic models,the rise in recompaction pressure has the most significant effect on the rise in the volumes of steam injection and water production,both rock compressibility and recompaction pressure are positively correlated with steam injectivity and oil/water production,and the dilation pressure is negatively correlated with steam injectivity and oil/water production.In the linear model,dilation pressure has the most significant negative impact on the cumulative oil production,and compressibility and recompaction pressure are positively correlated with oil production.In the quadratic model,the rise in recompaction pressure has the most significant effect on the rise in the cumulative volumes of oil/water production and steam injection.The interactions between the dilation/recompaction pressures and spongy-rock compressibility negatively affect the cumulative volumes of oil/water production and steam injection.

A 3D attention U-Net network and its application in geological model parameterization

To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.

Numerical Simulation of Oil and Gas Two-Phase Flow in Deep Condensate Gas Reservoirs in Bohai Buried Hills

The BZ19-6 gas field is characterized by high temperature and high pressure (HTHP), high condensate content, little difference between the formation pressure and dew point pressure, and large amount of reverse condensate liquid. During the early stage of depletion development, the production gas-oil ratio (GOR) and production capacity remain relatively stable, which is inconsistent with the conventional reverse condensate seepage law. In view of the static and dynamic conflict in development and production, indoor high-temperature and high-pressure PVT experiment was carried out to reveal the mist-like condensation phenomenon of fluids in the BZ19-6 formation. And the seepage characteristics of condensate gas reservoirs with various degrees of depletion under the condition of HTHP were analyzed based on production performance. The change rule of fluid phase state was analyzed in response to the characterization difficulties of the seepage mechanism. The fluid state was described using the miscible mechanism. And the interphase permeability interpolation coefficient was introduced based on interfacial tension. By doing so, the accurate characterization of the “single-phase flow of condensate gas-near-miscible mist-like quasi single-phase flow-oil-gas two-phase flow” during the development process was achieved. Then the accurate fitting of key indicators for oilfield development was completed, and the distribution law of formation pressure and the law of condensate oil precipitation under different reservoir conditions are obtained. Based on research results, the regulation strategy of variable flow rate production was developed. Currently, the work system has been optimized for 11 wells, achieving a “zero increase” in the GOS of the gas field and an annual oil increase of 22,000 cubic meters.

A review of closed-loop reservoir management

The closed-loop reservoir management technique enables a dynamic and real-time optimal production schedule under the existing reservoir conditions to be achieved by adjusting the injection and production strategies. This is one of the most effective ways to exploit limited oil reserves more economically and efficiently. There are two steps in closed-loop reservoir management： automatic history matching and reservoir production opti- mization. Both of the steps are large-scale complicated optimization problems. This paper gives a general review of the two basic techniques in closed-loop reservoir man- agement; summarizes the applications of gradient-based algorithms, gradient-free algorithms, and artificial intelligence algorithms; analyzes the characteristics and application conditions of these optimization methods; and finally discusses the emphases and directions of future research on both automatic history matching and reservoir production optimization.

Naturally fractured hydrocarbon reservoir simulation by elastic fracture modeling

Accurate fluid flow simulation in geologically complex reservoirs is of particular importance in construction of reservoir simulators.General approaches in naturally fractured reservoir simulation involve use of unstructured grids or a structured grid coupled with locally unstructured grids and discrete fracture models.These methods suffer from drawbacks such as lack of flexibility and of ease of updating.In this study,I combined fracture modeling by elastic gridding which improves flexibility,especially in complex reservoirs.The proposed model revises conventional modeling fractures by hard rigid planes that do not change through production.This is a dubious assumption,especially in reservoirs with a high production rate in the beginning.The proposed elastic fracture modeling considers changes in fracture properties,shape and aperture through the simulation.This strategy is only reliable for naturally fractured reservoirs with high fracture permeability and less permeable matrix and parallel fractures with less cross-connections.Comparison of elastic fracture modeling results with conventional modeling showed that these assumptions will cause production pressure to enlarge fracture apertures and change fracture shapes,which consequently results in lower production compared with what was previously assumed.It is concluded that an elastic gridded model could better simulate reservoir performance.

A smart productivity evaluation method for shale gas wells based on 3D fractal fracture network model

The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility.

Applications of smart proxies for subsurface modeling

Using artificial intelligence(AI) and machine learning(ML) techniques, we developed and validated the smart proxy models for history matching of reservoir simulation, sensitivity analysis, and uncertainty assessment by artificial neural network(ANN). The smart proxy models were applied on two cases, the first case study investigated the application of a proxy model for calibrating a reservoir simulation model based on historical data and predicting well production while the second case study investigated the application of an ANN-based proxy model for fast-track modeling of CO2 enhanced oil recovery, aiming at the prediction of the reservoir pressure and phase saturation distribution at injection stage and post-injection stage. The prediction effects for both cases are promising. While a single run of basic numerical simulation model takes hours to days, the smart proxy model runs in a matter of seconds, saving 98.9% of calculating time. The results of these case studies demonstrate the advantage of the proposed workflow for addressing the high run-time, computational time and computational cost of numerical simulation models. In addition, these proxy models predict the outputs of reservoir simulation models with high accuracy.

Performance Evaluation of Ionic Liquids Using Numerical Simulation

Given the rise in oil productivity from conventional and unconventional resources in Canada using Enhanced Oil Recovery (EOR), the need to understand and characterize these techniques, for the purpose of recovery optimization, has taken a prominent role in resource management. Chemical flooding has proved to be one of the most efficient EOR techniques. This study investigated the potential of employing Ionic Liquids (ILs) as alternative chemical agents for improving oil recovery. There is very little attention paid to employing this technique as well as few experimental and simulation studies. Consequently, very limited data are available. Since pilot and field studies are relatively expensive and time consuming, a numerical simulation study using CMG-STARS simulator was utilized to explore the efficiency of employing 1-Ethyl-3-Methyl-Imidazolium Acetate ([EMIM][Ac]) and 1-Benzyl-3-meth- limidazolium chloride ([BenzMIM][Cl]) with respect to improving medium oil recovery. Eight different lab-scale sandpack flooding experiments were selected to develop a numerical model to obtain the history matching of the experimental flooding results using CMG-CMOST. We observed that the main challenge was tuning the relative permeability curves to achieve a successful match for the oil recovery factor. Finally, a sensitivity study was performed to examine the effect of the chemical injection rate, the chemical concentration, the slug size, and the initiation time on oil recovery. The results showed a noticeable increase in the oil RF when injecting IL compared to conventional waterflooding.