A Machine Learning Approach to Optimize Shale Gas Supply Chain Networks
- H. I. Asala (Louisiana State University) | J. Chebeir (Louisiana State University) | W. Zhu (Louisiana State University) | I. Gupta (Louisiana State University) | A. Dahi Taleghani (Louisiana State University) | J. Romagnoli (Louisiana State University)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 9-11 October, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2017. Society of Petroleum Engineers
- 5.8.2 Shale Gas, 6.1.5 Human Resources, Competence and Training, 5.5 Reservoir Simulation, 5.5.8 History Matching, 7 Management and Information, 3.2.4 Produced Water Management, 7.6.7 Neural Networks, 5.8 Unconventional and Complex Reservoirs, 3 Production and Well Operations, 2.4 Hydraulic Fracturing, 4.6 Natural Gas, 1.6 Drilling Operations, 2 Well completion, 7.6 Information Management and Systems, 3.2 Well Operations and Optimization, 5 Reservoir Desciption & Dynamics, 3 Production and Well Operations, 6.1 HSSE & Social Responsibility Management, 5.8.4 Shale Oil, 7.6.6 Artificial Intelligence, 6 Health, Safety, Security, Environment and Social Responsibility
- Recurrent Neural Network, Compositional Modeling, Re-fracturing and Candidate Selection, Mixed Integer Non-Linear Programming, Forecasted Natural Gas Demand
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The unsteady recovery of oil and gas prices in early 2017 led to an increase in drilling and hydraulic fracturing operations in North America liquid-rich shale plays. The increasing number of producing wells, in addition to re-fractured wells, impose the need for optimizing field development strategies and shale gas supply chain networks that maximize profitability. Moreover, operators must account for undulating natural gas demands both locally and externally in a persistently low oil price environment. In this paper, we adopt supervised machine learning approaches to forecast local natural gas demand as well as guide well re-frac candidature. Both serve as critical inputs for maximizing the net present value of any shale gas field development project. An optimized water management structure is also incorporated in the proposed framework to account for associated produced-water recycling.
Considering a shale gas network in the Marcellus play, supply chain optimization was achieved using a mixed integer non-linear programming formulation. The Strategic Planning model relies on at least 4 major efforts including reservoir simulation, which in turn relies on output from a feed forward Neural Network (NN) algorithm. The trained NN algorithm was deemed suitable for recommending re-frac candidates, necessary decision variables for multiphase reservoir simulation. Finally, NPV optimization relied on a four-layer Long Short-Term Memory (LSTM) recurrent neural network, developed for forecasting local shale gas demand. Both neural network algorithms were scripted using python.
17 non-redundant parameters were mined for 250 wells in the case study. The t-Distributed Stochastic Neighbor Embedding technique was used to visualize related low dimensional manifolds within the high-dimensional data set, and a NN algorithm was used to obtain probabilities of misclassification. The well-pads in the case study superstructure are modeled using compositional reservoir simulation. Each reservoir model is tuned by history matching production and pressure data for designated producing wells. Alternative field development strategies (including re-fracturing) are then simulated for a 10-year planning horizon to generate gas and water rate decline profiles. Using the LSTM developed, local gas demand is forecasted using data sets created from multivariate time-dependent local and global variables affecting shale gas demand. The LSTM algorithm is derived by convolving some features from the raw data set, with fined-tuned weights, with the objective of minimizing a pre-defined demand error function. The results obtained show that application of this integrated approach can give operators a calculated advantage, preventing erroneous feedback of project profitability but allowing early time decision-making that maximizes shale asset NPV.
|File Size||3 MB||Number of Pages||29|
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