A Machine Learning-Based Field-Scale Reservoir Analysis Method

Wu, Xiongjun; Shih, Chung-Yan; Liu, Guoxiang; Mark-Moser, MacKenzie; Patrick, Wingo,

doi:10.2139/ssrn.4272874

Cited by 1 publication

(1 citation statement)

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“…All ML models in this study were constructed using long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks [16]. These models take advantage of LSTM s ability to handle temporal data, i.e., the time-series data in this study, and utilize MLP for feature learning and feature extraction.…”

Section: Model Structurementioning

confidence: 99%

Unconventional Wells Interference: Supervised Machine Learning for Detecting Fracture Hits

Liu,

Wu,

Romanov

2024

Applied Sciences

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The primary objective of the study was development of a machine learning (ML)-based workflow for fracture hit (“frac hit”) detection and monitoring using shale oil-field data such as drilling surveys, production history (oil and produced water), pressure, and fracking start time and duration records. The ML method takes advantage of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks to identify the frac hits due to hydraulic communication between the fracking child well(s) and the producing parent well(s) within the same pad (intra-pad interaction) and/or on different pads (inter-pad interaction). It utilizes time series of pressure and production data from within a pad and from adjacent pads. The workflow can capture time variable features of frac hits when the model architecture is deep and wide enough, with enough trainable parameters for deep learning and feature extraction, as demonstrated in this paper by using training and testing subsets of the field data from selected neighboring pads with over a couple of hundred wells. The study was focused on frac-hit interaction among paired wells and demonstrated that the ML model, once trained, can predict the frac-hit probability.

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Section: Model Structurementioning

confidence: 99%