Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Abstract

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation.

Machine Learning Models for Equivalent Circulating Density Prediction from Drilling Data

“…In the oil and gas industry, many studies utilized ML techniques for finding solutions for practical challenges. − Intelligent models were accomplished by AI tools for many purposes such as identifying the formation lithology, predicting the formation and fracture pressures, estimating the properties of reservoir fluids, estimating the oil recovery factor, , predicting the tops of the drilled formation, rate of penetration (ROP) prediction and optimization for different drilled formations and well profiles, − determining the content of total organic carbon, − and estimating the rock static Young’s modulus, − predicting the compressional and shear sonic times, determining the rock failure parameters, detecting the downhole abnormalities during horizontal drilling, determining the wear of a drill bit from the drilling parameters, and predicting the rheological properties of drilling fluids in real time. ,− …”

Data-Driven Modeling Approach for Pore Pressure Gradient Prediction while Drilling from Drilling Parameters

“…It has been widely used in petroleum engineering as it not only has the ability to solve complex problems and deal with the big data but also perfectly represents them with high accuracy compared to other models . Different models were introduced for different purposes such as ROP prediction and optimization for different drilled formations and well profiles, estimating the oil recovery factor, lithology classification, well planning, the formation lithology, prediction of formation tops, estimating the properties of reservoir fluids, fracture density estimation, detecting the downhole abnormalities during horizontal drilling, wellbore stability, predicting the compressional and shear sonic times, fracture pressure prediction while drilling, estimating the content of total organic carbon, identifying and estimating the rock failure parameters, estimating the wear of a drill bit from the drilling parameters, predicting the rheological properties of drilling fluids in real time, − and estimating the rock static Young’s modulus. , …”

“…20 Different models were introduced for different purposes such as ROP prediction and optimization for different drilled formations and well profiles, 21 estimating the oil recovery factor, 22 lithology classification, 23 well planning, 24 the formation lithology, 25 prediction of formation tops, 26 estimating the properties of reservoir fluids, 27 fracture density estimation, 28 detecting the downhole abnormalities during horizontal drilling, 29 wellbore stability, 30 predicting the compressional and shear sonic times, 31 fracture pressure prediction while drilling, 32 estimating the content of total organic carbon, 33 identifying and estimating the rock failure parameters, 34 estimating the wear of a drill bit from the drilling parameters, 35 predicting the rheological properties of drilling fluids in real time, 36−39 and estimating the rock static Young's modulus. 40,41 A few studies used machine learning tools to predict the pore pressure gradient. Ahmed et al 42 applied ANN to develop a pore pressure white box prediction model using seven parameters.…”

Intelligent Prediction of The Equivalent Circulating Density From Surface Data Sensors During Drilling By Employing Machine Learning Techniques

“…In the oil and gas industry, many studies utilized machine learning techniques for finding solutions for practical challenges [20][21][22][23] . Intelligent models were accomplished by artificial intelligence tools for many purposes as identifying the formation lithology 24 , predicting the formation and fracture pressures 25,26 , estimating the properties of reservoir fluids 27 , estimating the oil recovery factor 28,29 , predicting the tops of the drilled formation 30 , ROP prediction and optimization for different drilled formations and well profiles [31][32][33] , determining the content of total organic carbon [34][35][36] , and estimating the rock static Young's modulus [37][38][39][40] , predicting the compressional and shear sonic times 41 , determining the rock failure parameters 42, detecting the downhole abnormalities during horizontal drilling 43 , determining the wear of a drill bit from the drilling parameters 44 , and predicting the rheological properties of drilling fluids in real-time [45][46][47][48][49] .…”