Automatic history matching with data integration for unconventional reservoirs

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Date

2021-01-21

Authors

Liu, Chuxi

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Abstract

Given the dynamic production data of a reservoir, numerical optimization tools such as history matching can minimize the global error and find an optimal reservoir model that can approximate the fracture geometry and petrophysical parameters in the subsurface. For unconventional reservoirs, the idea behind the automatic history matching is well developed and the workflow is also applied to statistically generate an ensemble of solutions that quantitatively characterizes associated uncertainties. However, more uncertainties regarding fracture and reservoir properties could be further reduced by using available information. Therefore, the objective of this study is to minimize uncertainty when make realizations of shale reservoirs, by integration of data from geology and geomechanics. We utilized the developed automatic history matching (AHM) code and modified the proxy engine, by substituting the neural network (NN) model with XGBoost (XGBOOST) model. The XGBOOST is found to perform more efficiently and accurately than NN, when the size of the available dataset for training is small. Furthermore, the AHM workflow is capable of modelling non-uniform half-length of hydraulic fractures in the corner point gridding system and complex, realistic natural fracture distributions using the fractal theory. Both of these functionalities partially fulfill some degrees of reality, by mimicking the irregular half-length outputted from fracture modelling software and naturally occurring patterns often found at cores. We applied this innovative approach to actual shale gas and shale oil wells. We then found that by coupling additional data into the AHM process, the fracture geometries and petrophysical properties can be more accurately depicted. The obtained results are also highly assimilating with the field experience from the engineers. In addition, by studying natural fractures in the model, we found out that the connectivity between natural fractures and wellbore/hydraulic fractures plays an important role in determining the well’s EUR potential. This study is beneficial because more reliable and robust results based on geological/geomechanical information, along with non-deterministic realizations of reservoir and fractures, can provide invaluable guidance towards well spacing planning, EUR estimation and economic appraisal, and fracture design optimizations

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