A Simple and Cost-Effective Workflow for Engineered Perforations

Cottingham, Byron; Truax, Jerome; Roark, Sam; Padgham, Matt; Denney, Steve; An, Xiaoxuan

doi:10.15530/urtec-2018-2875442

Proceedings of the 6th Unconventional Resources Technology Conference 2018

DOI: 10.15530/urtec-2018-2875442

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A Simple and Cost-Effective Workflow for Engineered Perforations

Byron Cottingham

Jerome Truax

Sam Roark

et al.

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Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

2021

J. Phys.: Conf. Ser.

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In unconventional oil and gas resources, especially shale oil and gas resources with extremely low permeability and porosity, in order to develop effectively, it is necessary to establish perforation and multi-stage fracturing. The machine learning algorithm k-means clustering was used to cluster the five features selected to describe reservoir properties: shale content, porosity, total organic carbon content and those reflecting rock mechanical properties: Young’s modulus and Poisson’s ratio. The data were classified in a high-dimensional space to determine different perforation fracture stages. The classification algorithm XGboost was then used to predict different perforation stages using four conventional logging curves GR, NPRL, VP and DEN. The K-means clustering algorithm based on Euclidean distance can well classify the selected features in the high-dimensional space. The Hopkins statistics of clustering trend is 0.94, showing a good clustering trend. When the classification algorithm is used for prediction, the average accuracy is 0.92, the average recall rate is 0.90, and the average F1 score is 0.90, which can predict different perforated fracture stages well and optimize the design of the perforated fracture stage.

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Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

2021

J. Phys.: Conf. Ser.

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A Simple and Cost-Effective Workflow for Engineered Perforations

Cited by 1 publication

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Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

Multi-stage perforation and hydraulic fracture stage selection based on machine learning methods

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