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  • Society of Petroleum Engineers (SPE)  (16)
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  • Society of Petroleum Engineers (SPE)  (16)
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  • 1
    Online Resource
    Online Resource
    Historical Window-Enhanced Transfer Gaussian Process for Production Optimization
    Society of Petroleum Engineers (SPE) ; 2022
    In:  SPE Journal Vol. 27, No. 05 ( 2022-10-12), p. 2895-2912
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 27, No. 05 ( 2022-10-12), p. 2895-2912
    Abstract: As a crucial step of reservoir management, production optimization aims to make the optimal scheme for maximal economic benefit measured by net present value (NPV) according to reservoir states. Despite the remarkable success, more advanced methods that can get higher NPV with less time consumed are still in urgent need. One main reason for limiting the optimization performance of existing methods is that historical data cannot be fully used. For a practical reservoir, production optimization is generally implemented in multiple stages, and substantial historical data are accumulated. These hard-won data obtained with lots of time encapsulate beneficial optimization experience and in-depth knowledge of the reservoir. However, when encountered with an unsolved optimization task in new stages, most methods discard these historical data, optimize from scratch, and gradually regain the knowledge of the reservoir with massive time for “trial and error” to find the right optimization direction, which is time-consuming and affects their practical application. Motivated by this, a novel method named historical window-enhanced transfer Gaussian process (HWTGP) for production optimization is proposed in this paper. Each optimization stage is regarded as a time window, and the data in historical windows are adopted as a part of training data to construct the transfer Gaussian process (TGP), which guides the whole optimization process. To solve the high-dimensional feature of practical problems, the prescreening framework based on a dimension-reduction method named Sammon mapping is introduced. The main innovation of HWTGP is that like experienced engineers, it can extract beneficial reservoir knowledge from historical data and transfer it to the target production-optimization problem, avoiding massive time for “trial and error” and getting superior performance. Besides, HWTGP has a self-adaptive mechanism to avoid harmful and ineffective experience transfer when tasks in historical and current windows are unrelated. To verify the effectiveness of HWTGP, two reservoir models are tested 10 times independently and results are compared with those obtained by differential evolution (DE) and a surrogate-based method. Experimental results show that HWTGP can achieve the optimal well controls that can get the highest NPV, and has significantly enhanced convergence speed with excellent stability, proving the effectiveness of transferring historical data.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/209813-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2022
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 2
    Online Resource
    Online Resource
    Upscaling of Transport Properties in Complex Hydraulic Fracture Systems
    Society of Petroleum Engineers (SPE) ; 2023
    In:  SPE Journal Vol. 28, No. 03 ( 2023-06-14), p. 1026-1044
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 28, No. 03 ( 2023-06-14), p. 1026-1044
    Abstract: Fluid flow in complex fracture systems near wellbore is influenced by heterogeneous fluid pathway structure, proppant distribution, and stress-induced fracture aperture change. The current physical experiments and pore-scale simulations only study the multiphase flow properties of hydraulic fracture (HF) with no proppant while the multiphase flow properties of induced fracture network (IFN) and HF with proppant are not available. It is well known that the simplified “straightline” relative permeability model does not apply to multiphase flow in IFN and HF with proppant. Consequently, there is no upscaled relative permeability model that works. In this study, we develop the physics-driven level set lattice Boltzmann method (LS-LBM)-coupled model to study multiphase flow properties in complex fractures during injected water flowback and propose the upscaled relative permeability models of IFN and HF with proppant. The imaged HF is applied to generate HFs with different aperture and proppant distributions using morphology operation and discrete element method (DEM). The imaged IFN is further applied to generate IFN with different aperture distributions by image dilation. The oil/water interface at different drainage pressures is tracked by LS, and the resultant fluid distributions are applied to calculate each phase’s effective permeability by LBM. We found that the aperture variation coefficient difference leads to various fluid expansion patterns in IFN and HF. The oil/water interface moving pattern exhibits “face expansion” in IFN and HF while the oil/water interface moving pattern resembles “finger expansion” in HF with embedded proppant with notably larger aperture variation coefficient. The upscaled relative permeability model is further established considering channel tortuosity variation and pore structure difference based on LS-LBM simulation results.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/212849-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2023
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Global and Local Surrogate-Model-Assisted Differential Evolution for Waterflooding Production Optimization
    Society of Petroleum Engineers (SPE) ; 2020
    In:  SPE Journal Vol. 25, No. 01 ( 2020-02-17), p. 105-118
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 25, No. 01 ( 2020-02-17), p. 105-118
    Abstract: Surrogate models, which have become a popular approach to oil-reservoir production-optimization problems, use a computationally inexpensive approximation function to replace the computationally expensive objective function computed by a numerical simulator. In this paper, a new optimization algorithm called global and local surrogate-model-assisted differential evolution (GLSADE) is introduced for waterflooding production-optimization problems. The proposed method consists of two parts: (1) a global surrogate-model-assisted differential-evolution (DE) part, in which DE is used to generate multiple offspring, and (2) a local surrogate-model-assisted DE part, in which DE is used to search for the optimum of the surrogate. The cooperation between global optimization and local search helps the production-optimization process become more efficient and more effective. Compared with the conventional one-shot surrogate-based approach, the developed method iteratively selects data points to enhance the accuracy of the promising area of the surrogate model, which can substantially improve the optimization process. To the best of our knowledge, the proposed method uses a state-of-the-art surrogate framework for production-optimization problems. The approach is tested on two 100-dimensional benchmark functions, a three-channel model, and the egg model. The results show that the proposed method can achieve higher net present value (NPV) and better convergence speed in comparison with the traditional evolutionary algorithm and other surrogate-assisted optimization methods for production-optimization problems.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/199357-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2020
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 4
    Online Resource
    Online Resource
    Imbalanced Working States Recognition of Sucker Rod Well Dynamometer Cards Based on Data Generation and Diversity Augmentation
    Society of Petroleum Engineers (SPE) ; 2023
    In:  SPE Journal Vol. 28, No. 04 ( 2023-08-9), p. 1925-1944
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 28, No. 04 ( 2023-08-9), p. 1925-1944
    Abstract: During sucker rod pump production, there is a commonly seen problem of class imbalance, which refers to the differences in the amount of data accumulated under different working conditions. This problem in rod pump diagnosis can lead to unsatisfactory classification results of surface dynamometer cards under working conditions with fewer samples. Therefore, this study adopts the conditional generative adversarial nets (CGANs) improved by mini-batch method to address the problem of class imbalance. CGAN is an efficient method of multiclass data generation, which learns the properties of dynamometer cards by training the generator and discriminator networks. CGAN is modified by mini-batch strategy to avoid mode collapse and enable the interaction among input samples of discriminator, so that the generated dynamometer cards can be much more diverse. Results show that the shapes of generated dynamometer cards are basically consistent with those of real samples, and the structural similarity (SSIM) among the generated samples decreases, indicating that the generated dynamometer cards have more types of shape. Meanwhile, as the generated dynamometer cards become more diverse, their differences with real samples in data distribution are reduced, according to the calculation of sliced Wasserstein (SW) distance. Based on real and generated dynamometer cards, we developed the classifiers for working condition diagnosis of rod pump through convolutional neural network (CNN). The classification results of the validation set indicate that without the mini-batch method, the recall of generated categories for pump hitting down and leakage has increased by 12 and 5.3%, respectively; in contrast, with the mini-batch method, the recall has increased more obviously by 7, 24, and 2%, respectively, for gas lock, pump hitting down, and leakage. Our research results have demonstrated that the proposed method can effectively solve the problem of insufficient data accumulation in the oil field.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/214661-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2023
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 5
    Online Resource
    Online Resource
    Fourier Neural Operator for Solving Subsurface Oil/Water Two-Phase Flow Partial Differential Equation
    Society of Petroleum Engineers (SPE) ; 2022
    In:  SPE Journal Vol. 27, No. 03 ( 2022-06-16), p. 1815-1830
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 27, No. 03 ( 2022-06-16), p. 1815-1830
    Abstract: While deep learning has achieved great success in solving partial differential equations (PDEs) that accurately describe engineering systems, it remains a big challenge to obtain efficient and accurate solutions for complex problems instead of traditional numerical simulation. In the field of reservoir engineering, the current mainstream machine learning methods have been successfully applied. However, these popular methods cannot directly solve the problem of 2D two-phase oil/water PDEs well, which is the core of reservoir numerical simulation. Fourier neural operator (FNO) is a recently proposed high-efficiency PDE solution architecture that overcomes the shortcomings of the above popular methods, which can handle this type of PDE problem well in our work. In this paper, a deep-learning-based model is developed to solve three categories of problems controlled by the subsurface 2D oil/water two-phase flow PDE based on the FNO. For this complex engineering equation, we consider many factors, select characteristic variables, increase the dimension channel, expand the network structure, and realize the solution of the engineering problem. The first category is to predict the distribution of saturation and pressure fields by PDE parameters. The second category is the prediction of time series. The third category is for the inverse problem. It has achieved good results on both forward and inverse problems. The network uses fast Fourier transform (FFT) to extract PDE information in Fourier space to approximate differential operators, making the network faster and with greater physics significance. The model is mesh-independent and has good generalization, which also shows superresolution. Compared to the original FNO, we improve the network structure, add physical constraints to deal with boundary conditions (BCs), and use a shape matrix to control irregular boundaries. Also, we have improved the FFT module to make the transformation smoother. Compared with advanced deep learning-based solvers at different resolutions, the results show that this model overcomes some shortcomings of popular algorithms such as physics-informed neural networks (PINNs) and fully convolutional network (FCN) and has stronger accuracy and applicability. Our work has great potential in the replacement of traditional numerical methods with neural networks for reservoir numerical simulation.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/209223-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2022
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 6
    Online Resource
    Online Resource
    Multiscale-Network Structure Inversion of Fractured Media Based on a Hierarchical-Parameterization and Data-Driven Evolutionary-Optimization Method
    Society of Petroleum Engineers (SPE) ; 2020
    In:  SPE Journal Vol. 25, No. 05 ( 2020-10-15), p. 2729-2748
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 25, No. 05 ( 2020-10-15), p. 2729-2748
    Abstract: Efficient identification and characterization of fracture networks are crucial for the exploitation of fractured media such as naturally fractured reservoirs. Using the information obtained from borehole logs, core images, and outcrops, fracture geometries can be roughly estimated. However, this estimation always has uncertainty, which can be decreased using inverse modeling. Following the Bayes framework, a common practice for inverse modeling is to sample from the posterior distribution of uncertain parameters, given the observational data. However, a challenge for fractured reservoirs is that the fractures often occur on different scales, and these fractures form an irregular network structure that is difficult to model and predict. In this work, a multiscale-parameterization method is developed to model the fracture network. Based on this parameterization method, we present a novel history-matching approach using a data-driven evolutionary algorithm to explore the Bayesian posterior space and decrease the uncertainties of the model parameters. Empirical studies on hypothetical and outcrop-based cases demonstrate that the proposed method can model and estimate the complex multiscale-fracture network on a limited computational budget.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/201237-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2020
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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    Online Resource
  • 7
    Online Resource
    Online Resource
    An Interpretable Interflow Simulated Graph Neural Network for Reservoir Connectivity Analysis
    Society of Petroleum Engineers (SPE) ; 2021
    In:  SPE Journal Vol. 26, No. 04 ( 2021-08-11), p. 1636-1651
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 26, No. 04 ( 2021-08-11), p. 1636-1651
    Abstract: Reservoir connectivity analysis plays an essential role in controlling water cut in the middle and later stages of reservoir development. The traditional analysis methods, such as well test and tracer, may result in interruption and high reservoir development costs. Analyzing connectivity through history data is an advisable alternative method because the fluctuation of data reflects interwell interference. However, most of the former data-driven methods, such as capacitance and resistance model (CRM), estimate connectivity using formulas in relatively simple forms, leading to inadequate expression for underground interwell flow. In this paper, an interpretable recurrent graph neural network (GNN) is proposed to construct an interacting process imitating the real interwell flow regularity and overcoming the weakness in previous methods. In contrast, it is formed by a deep enough neural network structure with a relatively larger number of parameters when compared with the CRM model. In detail, this method makes the first use of both rate information and bottomhole pressure (BHP) to completely describe the hidden state of wells and the energy information exchanged among them, which are then continually updated in spatial and temporal ways. Meanwhile, a self-defined recurrent structure deals with the time lag and attenuation phenomenon as it records the residual energy from past timestamps. Finally, it calculates BHP for each production well with the manually specified production rate as extra input data. Detailed results are presented in two examples. Our proposed method shows significant advantages to other methods due to its reasonable structure and great ability to fit nonlinear mapping.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/205024-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2021
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 8
    Online Resource
    Online Resource
    A Classification-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Production Optimization under Geological Uncertainty
    Society of Petroleum Engineers (SPE) ; 2020
    In:  SPE Journal Vol. 25, No. 05 ( 2020-10-15), p. 2450-2469
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), Vol. 25, No. 05 ( 2020-10-15), p. 2450-2469
    Abstract: Multiobjective optimization (MOO) is a popular procedure for waterflooding optimization under geological uncertainty that maximizes the expectation of net present value (NPV) over all possible uncertainty models and minimizes the variance simultaneously. However, the optimization process involves a large number of decision variables, and the problem is computationally expensive. Surrogate-assisted evolutionary algorithms (SAEAs), which have proved to be an effective way to solve expensive problems, design computationally inexpensive functions to approximate each objective function. On the basis of characterization, we have designed an efficient multiobjective evolutionary algorithm (MOEA) to effectively deal with computationally expensive simulation-based optimization problems. The uniqueness of this algorithm is that it incorporates a Pareto-rank-learning scheme with surrogate-assisted infill criterion. The first is to introduce a multiclass error-correcting output codes (ECOC) model that directly predicts the dominance relationship between candidate solutions and prescreens, and the second is to train a radial-basis function (RBF) network that predicts the objective functions of prescreened solutions to calculate the hypervolume (HV) improvement that maintains convergence and diversity. Compared with typical surrogate-based methods, the developed method provides a classifier first that can enhance the accuracy in high dimensions and reduce computational complexity. To the best of our knowledge, the proposed method compares with state-of-the-art surrogate frameworks for multiobjective production-optimization problems. In this paper, the proposed approach is applied to two 200D benchmark problems and two synthetic reservoir models. The results show that the proposed method can achieve more comprehensive and efficient reservoir management (RM) with a higher convergence speed compared with traditional MOEAs and surrogate-assisted optimization methods.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/201229-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2020
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 9
    Online Resource
    Online Resource
    Pore-Scale Simulation of Fracture Propagation by CO2 Flow Induced in Deep Shale Based on Hydro-Mechanical Coupled Model
    Society of Petroleum Engineers (SPE) ; 2023
    In:  SPE Journal ( 2023-10-1), p. 1-16
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), ( 2023-10-1), p. 1-16
    Abstract: The depletion of conventional reservoirs has led to increased interest in deep shale gas. Hydraulic fracturing addresses the challenge of developing low-permeability shale, involving hydro-mechanical coupling fracture propagation mechanics. Supercritical CO2 (SC-CO2) has become a promising alternative to fracturing fluids due to its ability to be buried underground after use. The high temperature, pressure, and stress of deep shale lead to the flow of fracturing fluid to plastic deformation of rock, resulting in microfractures. In this paper, we simulate the fracture propagation process of deep shale fractured by SC-CO2 based on the coupling of the Darcy-Brinkman-Biot method, which adopts the Navier-Stokes-like equation to solve the free flow region, and the Darcy equation with Biot’s theory to solve flow in the matrix. To clearly probe the mechanism of deep fracturing from a microscopic perspective, the plastic rock property is taken into consideration. We investigate the effects of injection velocity, rock plastic yield stress, formation pressure, and gas slippage effect on fluid saturation and fracture morphology, and find that increasing the injection rate of fracturing fluid can form better extended fractures and complex fracture networks, improving the fracturing effect. Furthermore, we find that it is more appropriate to adopt SC-CO2 as a fracturing fluid alternative in deep shale with higher plastic yield stress due to higher CO2 saturation in the matrix, indicating greater carbon sequestration potential. High confining pressure promotes the growth of shear fractures, which are capable of more complex fracture profiles. The gas slip effect has a significant impact on the stress field while ignoring the flow field. This study sheds light on which deep shale gas reservoirs are appropriate for the use of SC-CO2 as a fracturing fluid and offers recommendations for how to enhance the fracturing effect at the pore scale.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/217990-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2023
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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  • 10
    Online Resource
    Online Resource
    Inversion Framework of Reservoir Parameters Based on Deep Autoregressive Surrogate and Continual Learning Strategy
    Society of Petroleum Engineers (SPE) ; 2023
    In:  SPE Journal ( 2023-06-1), p. 1-17
    Details
    In: SPE Journal, Society of Petroleum Engineers (SPE), ( 2023-06-1), p. 1-17
    Abstract: History matching is a crucial process that enables the calibration of uncertain parameters of the numerical model to obtain an acceptable match between simulated and observed historical data. However, the implementation of the history-matching algorithm is usually based on iteration, which is a computationally expensive process due to the numerous runs of the simulation. To address this challenge, we propose a surrogate model for simulation based on an autoregressive model combined with a convolutional gated recurrent unit (ConvGRU). The proposed ConvGRU-based autoregressive neural network (ConvGRU-AR-Net) can accurately predict state maps (such as saturation maps) based on spatial and vector data (such as permeability and relative permeability, respectively) in an end-to-end fashion. Furthermore, history matching must be performed multiple times throughout the production cycle of the reservoir to fit the most recent production observations, making continual learning crucial. To enable the surrogate model to quickly learn recent data by transferring experience from previous tasks, an ensemble-based continual learning strategy is used. Together with the proposed neural network–based surrogate model, the randomized maximum likelihood (RML) is used to calibrate uncertain parameters. The proposed method is evaluated using 2D and 3D reservoir models. For both cases, the surrogate inversion framework successfully achieves a reasonable posterior distribution of reservoir parameters and provides a reliable assessment of the reservoir’s behaviors.
    Type of Medium: Online Resource
    ISSN: 1086-055X , 1930-0220
    URL: Article
    DOI: 10.2118/215821-PA
    Language: English
    Publisher: Society of Petroleum Engineers (SPE)
    Publication Date: 2023
    detail.hit.zdb_id: 2375537-4
    SSG: 19,1
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