Production Performance Estimation from Stimulation and Completion Parameters using Machine Learning Approach in the Marcellus Shale

Abstract

Harnessing the power of predictive statistical modeling and advanced machine learning techniques to evaluate and assess the impact of various well completion and stimulation parameters on the wells' production performance have become one of the recent growing interest in the oil and gas industry and especially in the development of the unconventional resources. The main objective of this study is to utilize the partial least square (PLS), a machine learning technique, to create predictive models to evaluate the impact of several well completion parameters such as lateral length and gross perforated interval coupled with different stimulation parameters such as the total pumped water and sand volumes in addition to the concentrations of different stimulation additives. The completion and stimulation parameters will be used as a predictor variable to the short- and long-term gas production in the Marcellus shale play. Another outcome from this study is to gain insights about the relationship between those parameters and the wells production in order to optimize the future designs of those wells. Data of more than 2700 wells were utilized from two sources, the stimulation parameters were gathered from FracFocus website and the well completion and production data were gathered from DrillingInfo database. Three production predictive models were presented in the form of mathematical functions that were created based on the wells' data which can be considered as the training dataset. Unlike many other prediction modeling approaches, this study applies cross validation procedures to assure the reliability of the constructed prediction models.

Meeting Name

53rd U.S. Rock Mechanics/Geomechanics Symposium (2019: Jun. 23-26, Brooklyn, NY)

Department(s)

Geosciences and Geological and Petroleum Engineering

Keywords and Phrases

Additives; Functions; Gas industry; Learning algorithms; Machine learning; Oil field development; Oil well completion; Oil well production; Resource valuation; Rock mechanics; Shale; Well stimulation, Machine learning approaches; Machine learning techniques; Mathematical functions; Partial least square (PLS); Production performance; Statistical modeling; Stimulation parameters; Unconventional resources, Natural gas well completion

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 American Rock Mechanics Association (ARMA), All rights reserved.

Publication Date

01 Jun 2019

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