Abstract
Driven by advancements in technology, tight-gas field development has become a significant source of hydrocarbon to the energy industry. The amount of data generated in the process is immense as most platforms are now being digitized. Machine learning tools can be used to analyse this data in order to build patterns between several dependent and independent variables. Forecasting initial gas production rates has important implications in the planning production/processing facilities for new wells, affects investment decisions and is an important component of reporting to regulatory agencies. This study is based on the analysis of reservoir rock/fluid properties and selected well parameters to build decision-based models that can predict initial gas production rates for tight gas formations. In this study, two machine learning predictive models; Artificial Neural Network (ANN) and Generalized Linear Model (GLM), were used to determine the expected recovery rate of planned new wells. Production data was retrieved from 224 wells and used in developing the model. The results obtained from these models were then compared to the actual recorded initial gas production rate from the wells. Results from the analysis carried out revealed a Mean Square Error (MSE) of 1.57 on a GLM model whereas the ANN model gave an MSE of 1.24. Key Performance Index for the ANN model revealed that reservoir thickness had the highest (36.5%) contribution to the initial gas production rate followed by the flowback rate (29%). The reservoir/fluid properties contribution to the initial gas production rate was 53% while the hydraulic fracture parameters contribution to the initial gas production rate was 47%.
Highlights
As technological advancements continue to improve daily in the oil and gas sector, spurred by advancements in shale oil and gas development, smart field development, cheaper and more reliable data storage technologies have led to an increase in the amount of data captured in the industry
Two Machine Learning models have been presented in this study for the prediction of the initial gas production rate for tight gas reservoirs using selected reservoir and well parameters
The Artificial Neural Network (ANN) model with one hidden layer was built by cross-validation with a minimum Mean Square Error of 1.24 while the Generalized Linear Model (GLM) model gave a Mean Square Error of 1.57
Summary
As technological advancements continue to improve daily in the oil and gas sector, spurred by advancements in shale oil and gas development, smart field development, cheaper and more reliable data storage technologies have led to an increase in the amount of data captured in the industry. In developing tight gas formations, hydraulic fracturing is used to produce fractures in rock formations which stimulate the flow of natural gas Reservoir modelling in such systems is an extremely complicated task, given the need to simulate fluid flow in a network of induced natural fractures coupled to geo-mechanical effects and other processes such as water blocking, non-Darcy flow in nano-scale pores, and adsorption/desorption (Cipolla et al, 2010 and Ding et al, 2014). Oil and gas production companies use thousands of sensors installed in the subsurface and surface facilities to provide continuous data collection, real-time monitoring of assets and the environmental conditions (Abdelkadir and Luc, 2014). This data comes in structured, semi-structured and unstructured forms. Machine learning in recent times has been successfully employed in different fields where huge amounts of data are prevalent to gen-
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