Production Forecasting for Shale Gas Reservoirs with Nanopores and Complex Fracture Geometries Using An Innovative Non-Intrusive EDFM Method

2018 ◽  
Author(s):  
Wei Yu ◽  
Kan Wu ◽  
Malin Liu ◽  
Kamy Sepehrnoori ◽  
Jijun Miao
Keyword(s):  
Shale Gas ◽  
Gas Reservoirs ◽  
Complex Fracture ◽  
Production Forecasting ◽  
Shale Gas Reservoirs

scholarly journals A New Approach for Automatic Optimization of Complex Fracture Network in Shale Reservoirs

Lithosphere ◽  
2021 ◽  
Vol 2021 (Special 1) ◽  
Author(s):  
Haibo Wang ◽  
Tong Zhou ◽  
Fengxia Li
Keyword(s):  
Shale Gas ◽  
Flow Model ◽  
Optimization Method ◽  
Fracture Network ◽  
Gas Reservoirs ◽  
Fracture Length ◽  
Complex Fracture ◽  
Shale Gas Reservoirs ◽  
Automatic Optimization ◽  
Complex Fracture Network

Abstract Shale gas reservoirs have gradually become the main source for oil and gas production. The automatic optimization technology of complex fracture network in fractured horizontal wells is the key technology to realize the efficient development of shale gas reservoirs. In this paper, based on the flow model of shale gas reservoirs, the porosity/permeability of the matrix system and natural fracture system is characterized. The fracture network morphology is finely characterized by the fracture network expansion calculation method, and the flow model was proposed and solved. On this basis, the influence of matrix permeability, matrix porosity, fracture permeability, fracture porosity, and fracture length on the production of shale gas reservoirs is studied. The optimal design of fracture length and fracture location was carried, and the automatic optimization method of complex fracture network parameters based on simultaneous perturbation stochastic approximation (SPSA) was proposed. The method was applied in a shale gas reservoir, and the results showed that the proposed automatic optimization method of the complex fracture network in shale gas reservoirs can automatically optimize the parameters such as fracture location and fracture length and obtain the optimal fracture network distribution matching with geological conditions.

A New Probabilistic Approach for Uncertainty Quantification in Well Performance of Shale Gas Reservoirs

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 2038-2048 ◽  
Author(s):  
Wei Yu ◽  
Xiaosi Tan ◽  
Lihua Zuo ◽  
Jenn-Tai Liang ◽  
Hwa C. Liang ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Shale Gas ◽  
Probabilistic Approach ◽  
Model Parameters ◽  
Well Performance ◽  
Gas Reservoirs ◽  
Production Forecasting ◽  
Shale Gas Reservoirs ◽  
Decline Curve ◽  
Curve Model

Summary Over the past decade, technological advancements in horizontal drilling and multistage fracturing enable natural gas to be economically produced from tight shale formations. However, because of limited availability of the production data as well as the complex gas-transport mechanisms and fracture geometries, there still exist great uncertainties in production forecasting and reserves estimation for shale gas reservoirs. The rapid pace of shale gas development makes it important to develop a new and efficient probabilistic-based methodology for history matching, production forecasting, reserves estimates, and uncertainty quantification that are critical for the decision-making processes. In this study, we present a new probabilistic approach with the Bayesian methodology combined with Markov-chain Monte Carlo (MCMC) sampling and a fractional decline-curve (FDC) model to improve the efficiency and reliability of the uncertainty quantification in well-performance forecasting for shale gas reservoirs. The FDC model not only can effectively capture the long-tail phenomenon of shale gas-production decline curves but also can obtain a narrower range of production prediction than the classical Arps model. To predict the posterior distributions of the decline-curve model parameters, we use a more-efficient adaptive Metropolis (AM) algorithm in place of the standard Metropolis-Hasting (MH) algorithm. The AM algorithm can form the Markov chain of decline-curve model parameters efficiently by incorporating the correlation between the model parameters. With the predicted posterior distributions of the FDC model parameters generated by the AM algorithm, the uncertainty in production forecasts and estimated-ultimate-recovery (EUR) prediction can then be quantified. This work provides an efficient and robust tool that is based on a new probabilistic approach for production forecasting, reserves estimations, and uncertainty quantification for shale gas reservoirs.

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