Fact-Based Re-Frac Candidate Selection and Design in Shale - A Case Study in Application of Data Analytics

Mohaghegh, Shahab D.

doi:10.15530/urtec-2016-2433427

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…This technology shows how to apply prediction analysis to shale and practice it in the Marcellus shale block. It proves that the data generated in the initial development process can become an information base for well selection during refracturing (Mohaghegh, 2016).…”

Section: Refracturing Candidate Wellsmentioning

confidence: 78%

Progress and Enlightenment of Refracturing Technology for Shale Gas in North America

Lei

Pan²,

Huang³

et al. 2022

Front. Energy Res.

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Section: Refracturing Candidate Wellsmentioning

confidence: 78%

Progress and Enlightenment of Refracturing Technology for Shale Gas in North America

Lei

Pan²,

Huang³

et al. 2022

Front. Energy Res.

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“…11. Identify and rank re-frac candidate wells, and recommend most appropriate completion design [1].…”

Section: Shale Analyticsmentioning

confidence: 99%

Shale Analytics: Making Production and Operational Decisions Based on Facts: A Case Study in Marcellus Shale

Mohaghegh

Gaskari

Maysami

2017

Day 3 Thu, January 26, 2017

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Managers, geologists, reservoir and completion engineers are faced with important challenges and questions when it comes to producing from and operating shale assets. Some of the important questions that need to be answered are: What should be the distance between wells (well spacing)? How many clusters need to be included in each stage? What is the optimum stage length? At what point we need to stop adding stages in our wells (what is the point of diminishing returns)? At what rate and at what pressure do we need to pump the fluid and the proppant? What is the best proppant concentration? Should our completion strategy be modified when the quality of the shale (reservoir characteristics) and the producing hydrocarbon (dry gas, vs. condensate rich, vs. oil) changes in different parts of the field? What is the impact of soak time (starting production right after the completion versus delaying it) on production? Shale Analytics is the collection of the state of the art data driven techniques including artificial intelligence, machine learning, and data mining that addresses the above questions based on facts (field measurements) rather than human biases. Shale Analytics is the fusion of domain expertise (years of geology, reservoir, and production engineering knowledge) with data driven analytics. Shale Analytics is the application of Big Data Analytics, Pattern Recognition, Machine Learning and Artificial Intelligence to any and all Shale related issues. Lessons learned from the application of Shale Analytics to more than 3,000 wells in Marcellus, Utica, Niobrara, and Eagle Ford is presented in this paper along with a detail case study in Marcellus Shale. The case study details the application of Shale Analytics to understand the impact of different reservoir and completion parameters on production, and the quality of predictions made by artificial intelligence technologies regarding the production of blind wells. Furthermore, generating type curves, performing "Look-Back" analysis and identifying best completion practices are presented in this paper. Using Shale Analytics for re-frac candidate selection and design was presented in a previous paper [1].

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“…Tahmasebi developed data mining and machine learning algorithms to assist in identifying the optimal reservoirs for shale reservoirs, that is, locations with a high total organic carbon (TOC) content and fracturable brittle rocks [14]. Mohaghegh [15,16] used cutting-edge machine learning to analyze the McVey et al [17] constructed, trained, and implemented an artificial neural network to evaluate and discuss the application of this new technology to hydraulic fracture design and evaluation. design and evaluation of fracturing.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

Yue¹,

Luo²

2022

Fluid Dynamics &Amp; Materials Processing

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Because carbonate rocks have a wide range of reservoir forms, a low matrix permeability, and a complicated seam hole formation, using traditional capacity prediction methods to estimate carbonate reservoirs can lead to significant errors. We propose a machine learning-based capacity prediction method for carbonate rocks by analyzing the degree of correlation between various factors and three machine learning models: support vector machine, BP neural network, and elastic network. The error rate for these three models are 10%, 16%, and 33%, respectively (according to the analysis of 40 training wells and 10 test wells).

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Fact-Based Re-Frac Candidate Selection and Design in Shale - A Case Study in Application of Data Analytics

Cited by 8 publications

References 14 publications

Progress and Enlightenment of Refracturing Technology for Shale Gas in North America

Progress and Enlightenment of Refracturing Technology for Shale Gas in North America

Shale Analytics: Making Production and Operational Decisions Based on Facts: A Case Study in Marcellus Shale

Machine Learning-Based Prediction of Oil-Water Flow Dynamics in Carbonate Reservoirs

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