Real-Time Prediction for Sonic Slowness Logs from Surface Drilling Data Using Machine Learning Techniques

Abstract

Acoustic data obtained from sonic logging tools plays an important role in formation evaluation. Given the associated costs, however, the industry clearly stands to benefit from cheaper technologies to obtain compressional and shear wave slowness data. Therefore, this paper delineates an alternative solution for the prediction of sonic log data by means of Machine Learning (ML). This study takes advantage of an adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) ML techniques to predict compressional and shear wave slowness from drilling data only. In particular, the network is trained utilizing 2000 data points such as weight on bit (WOB), rate of penetration (ROP), standpipe pressure (SPP), torque (T), drill pipe rotation (RPM), and mud flow rate (GPM). Consequently, acoustic properties of the rock can be estimated solely from readily available parameters thereby saving both costs and time associated with sonic logs. The obtained results are promising and supportive of both ANFIS and SVM model as viable alternatives to obtain sonic data without the need for running sonic logs. The developed ANFIS model was able to predict compressional and shear wave slowness with correlation coefficients of 0.94 and 0.98 and average absolute percentage errors (AAPE) of 1.87% and 2.61%, respectively. Similarly, the SVM model predicted sonic logs with high accuracy yielding to correlation coefficients of more than 0.98 and AAPE of 0.74% and 0.84% for both compressional and shear logs, respectively. Once a network is trained, the approach naturally lends itself to be integrated as a real time service. This study outlines a novel and cost-effective solution to estimate rock compressional and shear-wave slowness solely from readily available drilling parameters. Importantly, the model has been verified for wells drilled in different formations with complex lithology substantiating the effectiveness of the approach.

Prediction of Compressional Slowness from Conventional Well Log Data using the Gradient Boosting Algorithm

“…Sonic log tests are not only performed in old wells but also in newly created wells because they are one of the measurements that take a lot of time and money. [2]. Due to the complex subsurface stratigraphy, poor drilling conditions (washout), inadequate logging tools, relatively tricky, time-consuming, and poor subsurface correlation with offset wells, good sonic logs are scarce [3,4].…”

Enhancing Shear Velocity Log Prediction in Ahnet Field, Algeria, Through Well Logs and Machine Learning Techniques

“…On the other hand, machine learning algorithms have proven their efficiency and accurately identify the relationship between non-linear complex phenomena (Hamadi et al, 2023). ML algorithms have better generalization ability and can discover and extract hidden trends and relationships from huge datasets that were previously impossible to explore manually (Suleymanov et al, 2021). Machine learning techniques have been used successfully for a wide range of tasks in the oil and gas industry, from exploration and geophysics applications to production, such as seismic data processing (Karrenbach et al, 2000), ROP prediction (Moran et al, 2010), UCS prediction (Chellal et al 2023), drilling optimization (Ouadi et al, 2023), water saturation prediction , mineralogy prediction (Laalam et al, 2022), stress-dependent porosity and permeability prediction (Ouadi et al, 2022), enhanced oil recovery applications (Chemmakh et al, 2021), and completion design (Laoufi et al, 2022), Therefore, our study seeks to investigate the integration of machine learning and well logs for shear velocity prediction in the Ahnet field, Algeria.…”