bluuebunny/crossref_metadata_2025_split huggingface dataset:
High Performance Computing and Speedup Techniques in Geochemical Modeling of Matrix Acidizing
Authors: Wei Wan , Sanaei Alireza , Bordeaux Rego Fabio , Sepehrnoori Kamy 
Published: March 2023

Abstract
Matrix acidizing is a stimulation treatment during which acid is injected below formation fracture pressure. The purpose of acidizing is to enlarge pore space or create channels through dissolution of plugging particles and formation minerals near the wellbore. Simulation of acidizing process is computationally expensive, especially for geochemical simulation which considers full-species transport and complex reactions. In this paper, geochemical modeling of acidizing process is implemented through coupling two simulation models. One is UTCOMP (a 3D reservoir simulator) which is responsible for calculations of fluid flow and solute transport. The other is IPhreeqc (a geochemical package) which is responsible for calculations of kinetic and equilibrium reactions among minerals and aqueous species. Acidizing simulation through the coupled model UTCOMP-IPhreeqc is computationally expensive, and geochemical calculations through IPhreeqc are the computational bottleneck. To improve the computational efficiency, geochemical calculations which take up the majority of the computational time are parallelized. And speedup techniques are implemented to reduce the number of IPhreeqc calls through monitoring the amount change of geochemical components. We have validated the coupled model UTCOMP-IPhreeqc through comparison with the analytical solution in previous work. Parallel performance is measured by comparing total CPU time, CPU time spent on geochemical calculations, and speedup ratios among simulation runs using different processor numbers. For heterogeneous matrix, different dissolution patterns are generated under different injection rates, and the computational time varies depending on the total injection time and the average time step size. For different dissolution patterns, the overall speedup ratio is up to 6.69 when using 16 processors, reducing 85% of CPU time compared with the case using a single processor. The speedup ratio for geochemical calculations is up to 14.21 when using 16 processors, saving 93% of CPU time compared with the case using a single processor. Besides parallel computing, the speedup techniques also improve the computational efficiency, and obtain optimal performance for wormhole dissolution patterns in which most of the geochemical reactions occur in a localized volume. The computational time is reduced to 49% maintaining 96% accuracy compared with the case without using speedup techniques. The coupled model UTCOMP-IPhreeqc has the modeling ability of full-species transport and complex reactions. On this basis, the presented model significantly improves the computational efficiency of UTCOMP-IPhreeqc through parallel computing and speedup techniques reducing the computational time of geochemical calculations.
A Global-Convergent Newton Optimization Algorithm for the Phase Behavior Calculations with Capillary Pressure Effect for Tight Reservoir Fluids
Authors: Jing Hongbin , Liu Jianqiao , Pan Huanquan , Kuang Tie , Yin Zhilin , Li Bensheng 
Published: March 2023

Abstract
The thermodynamic behavior of a fluid in a tight reservoir differs from that in the conventional environment. A new phase equilibrium algorithm with capillary pressure is presented and formulated using the laws of thermodynamics. At a given temperature, volume, and moles with capillary pressure, this new algorithm is based on the Newton iteration and line search, which guarantees a global convergence. We obtain the Newton direction by utilizing the modified Cholesky factorization to ensure a descending direction and combine line search to facilitate the iterations in the feasible domain. The initial values of the new algorithm originate from Michelsen's two-sided method. All relevant derivatives are computed analytically and automatically through the Automatically Differentiable Expression Templates Library (ADETL), developed at Stanford University. The new algorithm is based on the Helmholtz free energy, and the corresponding energy surface will not be influenced by the pressure inequality between the liquid and vapor phases. We tested our algorithm on several fluids with different pore radii over a wide range of temperatures and total volumes, and no single calculation breakdown occurred. Meanwhile, the new algorithm can also determine the system phase status at a given temperature and pressure. We compared the results between the given temperature and volume and the given temperature and pressure. There is a dispute in effect of the derivatives of capillary pressure with respect to compositions on the phase equilibrium calculation in literature. We compared the results with and without the derivatives at a given temperature and volume and a given temperature and pressure. These results show that our new algorithm exhibits a good convergent performance and a robust solution even if the pore radius decreases to one nanometer, which indicates the potential of our algorithm for simulating the shale reservoir production process.
Nonlinearly Constrained Life-Cycle Production Optimization Using Sequential Quadratic Programming (SQP) With Stochastic Simplex Approximated Gradients (StoSAG)
Authors: Nguyen Quang , Onur Mustafa , Alpak Faruk Omer 
Published: March 2023

Summary
Life-cycle production optimization is a crucial component of closed-loop reservoir management, referring to optimizing a production-driven objective function via varying well controls during a reservoir's lifetime. When nonlinear-state constraints (e.g., field liquid production rate and field gas production rate) at each control step need to be honored, solving a large-scale production optimization problem, particularly in geological uncertainty, becomes significantly challenging. This study presents a stochastic gradient-based framework to efficiently solve a nonlinearly constrained deterministic (based on a single realization of a geological model) or a robust (based on multiple realizations of the geologic model) production optimization problem. The proposed framework is based on a novel sequential quadratic programming (SQP) method using stochastic simplex approximated gradients (StoSAG). The novelty is due to the implementation of a line-search procedure into the SQP, which we refer to as line-search sequential quadratic programming (LS-SQP). Another variant of the method, called the trust-region SQP (TR-SQP), a dual method to the LS-SQP, is also introduced. For robust optimization, we couple LS-SQP with two different constraint handling schemes; the expected value constraint scheme and minimum-maximum (min-max) constraint scheme, to avoid the explicit application of nonlinear constraints for each reservoir model. We provide the basic theoretical development that led to our proposed algorithms and demonstrate their performances in three case studies: a simple synthetic deterministic problem (a two-phase waterflooding model), a large-scale deterministic optimization problem, and a large-scale robust optimization problem, both conducted on the Brugge model. Results show that the LS-SQP and TR-SQP algorithms with StoSAG can effectively handle the nonlinear constraints in a life-cycle production optimization problem. Numerical experiments also confirm similar converged ultimate solutions for both LS-SQP and TR-SQP variants. It has been observed that TR-SQP yields shorter but more safeguarded update steps compared to LS-SQP. However, it requires slightly more objective-function evaluations. We also demonstrate the superiority of these SQP methods over the augmented Lagrangian method (ALM) in a deterministic optimization example. For robust optimization, our results show that the LS-SQP framework with any of the two different constraint handling schemes considered effectively handles the nonlinear constraints in a life-cycle robust production optimization problem. However, the expected value constraint scheme results in higher optimal NPV than the min- max constraint scheme, but at the cost of possible constraint violation for some individual geological realizations.
Graphics Processing Unit Performance Scalability Study on a Commercial Black-Oil Reservoir Simulator
Authors: Tene Matei , Sekachev Michael , de Brito Dias Daniel , Szyndel Matthew D. E. 
Published: March 2023

Abstract
Commercial reservoir simulators have traditionally been optimized for distributed parallel execution on Central Processing Units (CPUs). Recent advances in Graphics Processing Units (GPUs) have led to the development of GPU-native simulators and triggered a shift towards a hardware-agnostic design in existing CPU solutions. For the latter, the suite of algorithms and data structures employed for a given computation are implemented for each target device. This results in a hybrid approach, where some simulator components inherently expose enough instruction parallelism or memory bandwidth requirements to warrant running on the GPU, while others are more suitable for the CPU. This paper examines the performance characteristics of a commercial black-oil reservoir simulator, which was recently extended with GPU support.
Each simulation case will distribute load on the various modules in a reservoir simulator differently, depending on the target physical properties and the forecasted data desired. To assess this, the scalability of the simulator is measured in detail using the CPU and GPU, for components where both implementations are available, focusing on time spent during model initialization, property calculation, linearization, solver, field management and reporting. This is done using test cases which stress the simulator across several axes: grid resolution, different petrophysical property distributions, well count and the volume of reported data. The synthetic models which form the basis for these studies were designed to represent realistic reservoir engineering scenarios.
The results show that a static partition between CPU- and GPU-assigned tasks, as employed by default in the simulator, is performant for scenarios where the work dedicated to grid cell properties and linear solution vastly outnumbers the effort spent resolving well or aquifer connections, field management and reporting. This is expected for typical simulation cases. However, when one of the latter aspects becomes dominant, the balance can shift, leading to suboptimal hardware utilization. In conclusion, if performance across all possible inputs is to be maintained, then a fully-CPU-and-GPU-capable simulator is needed, employing a dynamic scheduling strategy, where the runtime data locality, volume and parallelism of the corresponding computations are all considered when determining the target device for each operation.
To the authors’ knowledge, a study on the scalability of a commercial reservoir simulator, across two different hardware architectures, has not previously been conducted to this level of detail. The results on realistic models are presented in the hope that they will contribute to the discussion surrounding the benefits of modern computing hardware for reservoir simulation and help drive deployment and design decisions for existing and future developments in both the commercial and academic spheres.
Implementation of Adaptive Localization for Enhancing Ensemble-Based History Matching in Hydrocarbon Reservoir Management
Authors: Pernalete Marta , Lia Oddvar , Popa Tudor , Hanea Remus 
Published: March 2023

Abstract
In reservoir management, the ensemble-based history matching is applied to quantify and update uncertainty in reservoir characterization with the main objective to support high quality decisions. However, the ensemble-based history matching could suffer from statistical problems that make the ensemble unable to quantify uncertainty statistically-correctly. Localization schemes can effectively solve the ensemble-based history matching problems. The aim of this work lies in the research about the practical pros and cons of adaptive over non-adaptive localization schemes, that has not been extensively tested in different settings for ensemble-based history matching of reservoir simulation models. This work proposes a general workflow to guide localization implementation and evaluation. The workflow is developed in five sections: i) the initial ensemble generation; ii) history matching with no localization; iii) history matching with non-adaptive localization (distance-based); iv) history matching with adaptive localization (correlation-based); and v) Comparative result analysis among history matching cases.
The main conclusion from this work is that the history matching with the adaptive localization scheme overperformed the history matching with no localization and with the non-adaptive localization scheme. The history matching with adaptive localization demonstrated to estimate the reservoir dynamics and heterogeneities with more accuracy that can enhance decision quality in reservoir management. In addition, the implemented adaptive localization scheme supports standardization in the implementation of localization. However, the spatial regions for updating of the non-adaptive localization scheme are more intuitive than correlations of the adaptive localization scheme. Although correlations of the adaptive localization scheme embed reservoir dynamics, the understanding and interpretation of the correlations are not as straightforward as spatial regions are. Therefore, the adaptive localization scheme requires understanding of the statistical method and interpreting correlations to build history matching ownership.
The study brought innovative guidelines to best practice for implementing non-adaptive and adaptive localization. The effectiveness of the workflows was tested, evaluated, and contributed to further developing and improving the history matching processes. The identified practical pros and cons will facilitate the adaptive localization implementation in Equinor.
Parameter Inversion in Geothermal Reservoir Using Markov Chain Monte Carlo and Deep Learning
Authors: Zhang Zhen , He Xupeng , Li Yiteng , AlSinan Marwa , Kwak Hyung , Hoteit Hussein 
Published: March 2023

AbstractTraditional history-matching process suffers from non-uniqueness solutions, subsurface uncertainties, and high computational cost. This work proposes a robust history-matching workflow utilizing the Bayesian Markov Chain Monte Carlo (MCMC) and Bidirectional Long-Short Term Memory (BiLSTM) network to perform history matching under uncertainties for geothermal resource development efficiently. There are mainly four steps. Step 1: Identifying uncertainty parameters. Step 2: The BiLSTM is built to map the nonlinear relationship between the key uncertainty parameters (e.g., injection rates, reservoir temperature, etc.) and time series outputs (temperature of producer). Bayesian optimization is used to automate the tuning process of the hyper-parameters. Step 3: The Bayesian MCMC is performed to inverse the uncertainty parameters. The BiLSTM is served as the forward model to reduce the computational expense. Step 4: If the errors of the predicted response between the high-fidelity model and Bayesian MCMC are high, we need to revisit the accuracy of the BiLSTM and the prior information on the uncertainty parameters. We demonstrate the proposed method using a 3D fractured geothermal reservoir, where the cold water is injected into a geothermal reservoir, and the energy is extracted by producing hot water in a producer. Results show that the proposed Bayesian MCMC and BiLSTM method can successfully inverse the uncertainty parameters with narrow uncertainties by comparing the inversed parameters and the ground truth. We then compare its superiority with models like PCE, Kriging, and SVR, and our method achieves the highest accuracy. We propose a Bayesian MCMC and BiLSTM-based history matching method for uncertainty parameters inversion and demonstrate its accuracy and robustness compared with other models. This approach provides an efficient and practical history-matching method for geothermal extraction with significant uncertainties.
Feature Extraction in Time-Lapse Seismic Using Deep Learning for Data Assimilation
Authors: Exterkoetter Rodrigo , Rachid Dutra Gustavo , Passos de Figueiredo Leandro , Bordignon Fernando , Anozé Emerick Alexandre , Moura Silva Neto Gilson 
Published: March 2023

Abstract
The assimilation of time-lapse (4D) seismic data is challenging with ensemble-based methods because of the massive number of data points. This situation requires an excessive computational time and memory usage during the model updating step. We addressed this problem using a deep convolutional autoencoder to extract the relevant features of 4D images and generate a reduced representation of the data. The architecture of the autoencoder is based on the well-known VGG-19 network, from which we take advantage of the transfer learning technique. Using a pre-trained model bypasses the need of large training datasets and avoids the high computational demand to train a deep network. For further improvements in the reconstruction of the seismic images, we apply a fine-tuning of the weights of the latent convolutional layer. We propose to use a fully convolutional architecture, which allows the application of distance-based localization during data assimilation with the Ensemble Smoother with Multiple Data Assimilation (ES-MDA). The performance of the proposed method is investigated in a synthetic benchmark problem with realistic settings. We evaluate the methodology with three variants of the autoencoder, each one with a different level of data reduction. The experiments indicate that it is possible to use latent representations with major data reductions without impairing the quality of the data assimilation. Additionally, we compare CPU and GPU implementations of the ES-MDA update step and show in another synthetic problem that the reduction in the number of data points obtained with the application of the deep autoencoder may provide a substantial improvement in the overall computation cost of the data assimilation for large reservoir models.
Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning
Authors: Nagao Masahiro , Datta-Gupta Akhil , Onishi Tsubasa , Sankaran Sathish 
Published: March 2023

Abstract
Routine well-wise injection/production data contain significant information which can be used for closed-loop reservoir management and rapid field decisions. Traditional physics-based numerical reservoir simulation can be computationally prohibitive for short-term decision cycles, and also requires detailed geologic model. Reduced physics models provide an efficient simulator free workflow, but often have a limited range of applicability. Pure machine learning models lack physical interpretability and can have limited predictive power. We propose a hybrid machine learning and physics-based approach for rapid production forecasting and reservoir connectivity characterization using routine injection/production and pressure data.
Our framework takes routine measurements such as injection rate and pressure data as input and multiphase production rates as output. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for pre-processing to obtain approximate solutions that feed it into a neural network as input. This physics-based input feature can reduce the model complexity and provide significant improvement in prediction performance. The second approach augments the residual terms in the neural network loss function with physics-based regularization that relies on the governing partial differential equations (PDE). Reduced physics models are used for the governing PDE to enable efficient neural network training. The regularization allows the model to avoid overfitting and provides better predictive performance.
Our proposed hybrid models are first validated using a 2D benchmark reservoir simulation case and then applied to a field-scale reservoir case to show the robustness and efficiency of the method. The hybrid models are shown to provide superior prediction performance than pure machine learning models and reduced physics models in terms of multiphase production rates. Specifically, in the second method, the trained hybrid neural network model satisfies the reduced physics model, making it physically interpretable, and provides inter-well connectivity in terms of well flux allocation. The flux allocation estimated from the hybrid model was compared with streamline-based flux allocation, and excellent agreement was obtained. By combining the reduced physics model with the efficacy of deep learning, model calibration can be done very efficiently without constructing a geologic model.
The proposed hybrid models with physics-based regularization and preprocessing provide novel approaches to augment data-driven models with underlying physics to build interpretable models for understanding reservoir connectivity between wells and robust future production forecasting.
Application of Deep Neural Networks to the Operator Space of Nonlinear PDE for Physics-Based Proxy Modelling
Authors: Hadjisotiriou George , Mansour Pour Kiarash , Voskov Denis 
Published: March 2023

Abstract
In this study, we utilize deep neural networks to approximate operators of a nonlinear partial differential equation (PDE), within the Operator-Based Linearization (OBL) simulation framework, and discover the physical space for a physics-based proxy model with reduced degrees of freedom. In our methodology, observations from a high-fidelity model are utilized within a supervised learning scheme to directly train the PDE operators and improve the predictive accuracy of a proxy model. The governing operators of a pseudo-binary gas vaporization problem are trained with a transfer learning scheme. In this two-stage methodology, labeled data from an analytical physics-based approximation of the operator space are used to train the network at the first stage. In the second stage, a Lebesgue integration of the shocks in space and time is used in the loss function by the inclusion of a fully implicit PDE solver directly in the neural network's loss function. The Lebesgue integral is used as a regularization function and allows the neural network to discover the operator space for which the difference in shock estimation is minimal. Our Physics-Informed Machine Learning (PIML) methodology is demonstrated for an isothermal, compressible, two-phase multicomponent gas-injection problem. Traditionally, neural networks are used to discover hidden parameters within the nonlinear operator of a PDE. In our approach, the neural network is trained to match the shocks of the full-compositional model in a 1D homogeneous model. This training allows us to significantly improve the prediction of the reduced-order proxy model for multi-dimensional highly heterogeneous reservoirs. With a relatively small amount of training, the neural network can learn the operator space and decrease the error of the phase-state classification of the compositional transport problem. Furthermore, the accuracy of the breakthrough time prediction is increased therefore improving the usability of the proxy model for more complex cases with more nonlinear physics.