Rock Permeability Forecasts Using Machine Learning and Monte Carlo Committee Machines

Katz, Simon; Aminzadeh, Fred; Long, Wennan; Chilingar, George V.; Lackpour, Matin

doi:10.7569/jsee.2016.629519

Cited by 6 publications

(1 citation statement)

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“…A new version of feature engineering, named multilevel feature engineering is introduced and used for construction of predictor variables. Randomized Monte Carlo cross validation 10 is used in this paper as the tool for analysis of associations between SRV and constructed predictor variables and analysis of ML forecasts accuracy. Predictor variables used in ML forecasts were built using microseismic data available in the 3D HFTS dataset named "SUGG-A-171 5SM".…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Stimulated Reservoir Volume (SRV) Prediction Using 4-D Micro-seismic Data

Aminzadeh,

Katz

2024

MOJES

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New methodology of stable, high accuracy estimation and optimization of stimulated reservoir volume (SRV) forecast is presented in this paper. It includes time-related data segmentation, new multilevel feature engineering, analysis of associations and importance of engineered variables. Among first-level feature engineered variables are three quantile-type variables qRangeDepth, qRangeNorth, and qRangeEast. hese three quantile-type variables are used for SRV estimation. In addition to quantile-type variables, two first level variables -trange and event minute are constructed as the first -level variables. These two variables give compact characterization of distribution of microseismic events in time and are used as predictor variables in ML SRV forecast. Second and third level engineered variables are built via transformation of variables of the first level. Although in this paper we focus on the SRV forecast, the same ideas are applicable to the characterization and forecasting of the plume volume in carbon storage and monitoring applications. A linear regression method and two ML methods - random forest, and regression tree are used for the SRV forecast. It is demonstrated that in the case of selection of appropriate set of first and second level predictor variables even simplistic linear regression may produce accurate SRV forecasts. Still, machine learning methods produce more accurate forecasts characterized by high values of accuracy parameters r.squared and correlation between SRV and its forecast values. Our results can have a significant impact on the proper design of a hydraulic fracturing operation. It can also be used for monitoring CO2 plume in carbon sequestration sites.

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Section: Introductionmentioning

confidence: 99%