TY - JOUR
T1 - Data-driven acid fracture conductivity correlations honoring different mineralogy and etching patterns
AU - Aljawad, Murtada Saleh
AU - Desouky, Mahmoud
AU - Tariq, Zeeshan
AU - Alhoori, Hamed
AU - Mahmoud, Mohamed
AU - AlShehri, Dhafer
N1 - Generated from Scopus record by KAUST IRTS on 2023-09-20
PY - 2020/7/14
Y1 - 2020/7/14
N2 - Acid-fracturing operations are mainly applied in tight carbonate formations to create a highly conductive path. Estimating the conductivity of a hydraulic fracture is essential for predicting the fractured well productivity. Several models were developed previously to estimate the conductivity of acid-fractured rocks. In this research, machine learning methods were applied to 560 acid fracture experimental datapoints to develop several conductivity correlations that honor the rock types and etching patterns. Developing one universal correlation often results in significant error. To develop conductivity correlations, various data preprocessing methods were applied to remove the outliers and failed experiments. Features that did not contribute to precise predictions were removed through regularization. A machine learning classifier was built to predict the etching pattern based on the input data. We generated a multivariate linear regression model and compared it with other models generated through ridge regression. In addition to that, artificial neural network-based model was proposed to predict the fracture conductivity of several carbonate rocks such as dolomite, chalk, and limestone. The performance of the developed models was assessed using well-known metrics such as precision, accuracy, mean squared error, recall, and correlation coefficients. Cross-validation was also employed to assure accuracy and avoid overfitting. The classifier accuracy was 93%, while the regression model resulted in a relatively high correlation coefficient.
AB - Acid-fracturing operations are mainly applied in tight carbonate formations to create a highly conductive path. Estimating the conductivity of a hydraulic fracture is essential for predicting the fractured well productivity. Several models were developed previously to estimate the conductivity of acid-fractured rocks. In this research, machine learning methods were applied to 560 acid fracture experimental datapoints to develop several conductivity correlations that honor the rock types and etching patterns. Developing one universal correlation often results in significant error. To develop conductivity correlations, various data preprocessing methods were applied to remove the outliers and failed experiments. Features that did not contribute to precise predictions were removed through regularization. A machine learning classifier was built to predict the etching pattern based on the input data. We generated a multivariate linear regression model and compared it with other models generated through ridge regression. In addition to that, artificial neural network-based model was proposed to predict the fracture conductivity of several carbonate rocks such as dolomite, chalk, and limestone. The performance of the developed models was assessed using well-known metrics such as precision, accuracy, mean squared error, recall, and correlation coefficients. Cross-validation was also employed to assure accuracy and avoid overfitting. The classifier accuracy was 93%, while the regression model resulted in a relatively high correlation coefficient.
UR - https://pubs.acs.org/doi/10.1021/acsomega.0c02123
UR - http://www.scopus.com/inward/record.url?scp=85088595799&partnerID=8YFLogxK
U2 - 10.1021/acsomega.0c02123
DO - 10.1021/acsomega.0c02123
M3 - Article
SN - 2470-1343
VL - 5
SP - 16919
EP - 16931
JO - ACS OMEGA
JF - ACS OMEGA
IS - 27
ER -