Application of proxy-based MCMC and EDFM to history match a Vaca Muerta shale oil well

Dachanuwattana, Silpakorn; Jin, Junchen; Zuloaga-Molero, Pavel; Li, Xiangling; Xu, Yifei; Sepehrnoori, Kamy; Yu, Wei; Miao, Jijun

doi:10.1016/j.fuel.2018.02.018

Cited by 51 publications

(6 citation statements)

References 24 publications

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“…The efficiency of the EDFM method is much higher than the LGR method, especially when dealing with multiple wells with a large number of fractures [33]. It has been widely applied to model well interference due to complex fracture hits [34,35], automatic history matching for shale reservoirs [36][37][38], gas injection for enhanced unconventional oil recovery [39][40][41], and naturally fractured reservoir simulation [42].…”

Section: Methodsmentioning

confidence: 99%

Investigation of the Effect of Natural Fractures on Multiple Shale-Gas Well Performance Using Non-Intrusive EDFM Technology

Liu

et al. 2019

Energies

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The influence of complex natural fractures on multiple shale-gas well performance with varying well spacing is poorly understood. It is difficult to apply the traditional local grid refinement with structured or unstructured gridding techniques to accurately and efficiently handle complex natural fractures. In this study, we introduced a powerful non-intrusive embedded discrete fracture model (EDFM) technology to overcome the limitations of exiting methods. Through this unique technology, complex fracture configurations can be easily and explicitly embedded into structured matrix blocks. We set up a field-scale two-phase reservoir model to history match field production data and predict long-term recovery from Marcellus. The effective fracture properties were determined thorough history matching. In addition, we extended the single-well model to include two horizontal wells with and without including natural fractures. The effects of different numbers of natural fractures on two-well performance with varying well spacing of 200 m, 300 m, and 400 m were examined. The simulation results illustrate that gas productivity almost linearly increases with the number of two-set natural fractures. Furthermore, the difference of well performance between different well spacing increases with an increase in natural fracture density. A larger well spacing is preferred for economically developing the shale-gas reservoirs with a larger natural fracture density. The findings of this study provide key insights into understanding the effect of natural fractures on well performance and well spacing optimization.

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Section: Methodsmentioning

confidence: 99%

Investigation of the Effect of Natural Fractures on Multiple Shale-Gas Well Performance Using Non-Intrusive EDFM Technology

Liu

et al. 2019

Energies

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“…Offline methods build a sufficiently accurate surrogate model that can completely replace the role of numerical simulation and approximate the entire search space. Most studies (Dachanuwattana et al, 2018;Li et al, 2019) use sensitivity analysis methods to select some key parameters and then use traditional machine learning methods to construct alternative models. The emergence of deep learning makes it possible to directly establish the mapping from high-dimensional spatial parameters to reservoir dynamics without sensitivity analysis.…”

Section: Data-driven-based Surrogate Modelmentioning

confidence: 99%

Reservoir automatic history matching: Methods, challenges, and future directions

Liu

Zhang

Yao

2023

Adv. Geo-Energy Res.

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Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into modeldriven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.

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“…In recent years, the deep‐learning‐based surrogate model has been widely researched in inverse modeling, which can completely replace the role of numerical simulation and approximate the entire search space. Different from traditional studies that try to select the key parameters through sensitivity analysis and then use the machine learning method to establish the surrogate model (Bhark & Dehghani, 2014; Dachanuwattana et al., 2018; Li et al., 2019). The proposal of deep learning makes it possible to directly input high‐dimensional uncertain parameters into the surrogate model without sensitivity analysis to select key parameters.…”

Section: Introductionmentioning

confidence: 99%

Deep Conditional Generative Adversarial Network Combined With Data‐Space Inversion for Estimation of High‐Dimensional Uncertain Geological Parameters

Zhang

et al. 2023

Water Resources Research

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Inverse modeling aims to adjust the uncertain parameters of the geological model so that the numerical simulation results can reproduce the observed data and then provide reliable predictions, which is of great significance for the development of subsurface resources. Nowadays, inverse modeling is no longer limited to obtaining a single posterior model but evaluates the uncertainty of the predictions in terms of the bandwidth of dynamic responses by applying Monte Carlo methods to obtain a suite of equiprobable geological models (Arnold et al., 2013;Suzuki & Caers, 2006). Among the ensemble-based methods, the ensemble Kalman filter (EnKF; Aanonsen et al., 2009;Agbalaka & Oliver, 2008) is one of the most successful and effective methods. However, the continuous restart of the simulator may prevent the use of EnKF (Emerick & Reynolds, 2013a). Unlike EnKF, the ensemble smoother (ES; Van Leeuwen & Evensen, 1996) can avoid this problem by calculating a global update with all available observations. Nevertheless, the single update also hinders adequate data matching. More effective methods are proposed to overcome this problem by implementing the iterative forms of ES, such as the ES with multiple data assimilation (ESMDA) (Emerick & Reynolds, 2013a, 2013b). However, one intrinsic limitation is that these data assimilation methods are based on Gaussian assumptions, which makes them more suitable for geological parameters with the continuous spatial distribution. The posterior geological model parameters obtained by the data

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Application of proxy-based MCMC and EDFM to history match a Vaca Muerta shale oil well

Cited by 51 publications

References 24 publications

Investigation of the Effect of Natural Fractures on Multiple Shale-Gas Well Performance Using Non-Intrusive EDFM Technology

Investigation of the Effect of Natural Fractures on Multiple Shale-Gas Well Performance Using Non-Intrusive EDFM Technology

Reservoir automatic history matching: Methods, challenges, and future directions

Deep Conditional Generative Adversarial Network Combined With Data‐Space Inversion for Estimation of High‐Dimensional Uncertain Geological Parameters

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