Abstract
Synthetic well log generation using artificial intelligence tools is a robust solution for situations in which logging data are not available or are partially lost. Formation bulk density (RHOB) logging data greatly assist in identifying downhole formations. These data are measured in the field while drilling by using a density log tool in the form of either a logging while drilling (LWD) technique or (more often) by wireline logging after the formations are drilled. This is due to operational limitations during the drilling process. Therefore, the objective of this study was to develop a predictive tool for estimating RHOB while drilling using an adaptive network-based fuzzy interference system (ANFIS), functional network (FN), and support vector machine (SVM). The proposed model uses the mechanical drilling constraints as feeding input parameters, and the conventional RHOB log data as an output parameter. These mechanical drilling parameters are usually measured while drilling, and their responses vary with different formations. A dataset of 2400 actual datapoints, obtained from a horizontal well in the Middle East, were used to build the proposed models. The obtained dataset was divided into a 70/30 ratio for model training and testing, respectively. The optimized ANFIS-based model outperformed the FN- and SVM-based models with a correlation coefficient (R) of 0.93, and average absolute percentage error (AAPE) of 0.81% between the predicted and measured RHOB values. These results demonstrate the reliability of the developed ANFIS model for predicting RHOB while drilling, based on the mechanical drilling parameters. Subsequently, the ANFIS-based model was validated using unseen data from another well within the same field. The validation process yielded an AAPE of 0.97% between the predicted and actual RHOB values, which confirmed the robustness of the developed model as an effective predictive tool for RHOB.
Highlights
Formation density is considered one of the main factors in identifying the nature of subterranean formations [1]
The density logging tool was first presented in the petroleum industry in the 1960s
Gamma rays are emitted from the radioactive source, which thereafter interact with the electrons of the formation, resulting in a scattering of the rays
Summary
Formation density is considered one of the main factors in identifying the nature of subterranean formations [1]. The RHOB can assist in optimizing the drilling operation by improving bit selection, which is dependent on the nature of the formation being drilled It helps to avoid many disruptive problems such as the loss of circulation, kicks, and wellbore instability, by accurately detecting the downhole formations when drilling [10]. RHOB measurements may not be available during a drilling operation, and identifying the drilled formations while drilling can be confusing due to a lack of data There is another way to identify a drilled formation other than using logging data, which is analyzing the collected cuttings. The objective of this study was to develop a new approach by building novel models for predicting RHOB while drilling, using artificial intelligence tools such adaptive network-based fuzzy interference systems (ANFIS), functional networks (FN), and support vector machines (SVM) in conjunction with mechanical drilling parameters (i.e., ROP, WOB, T, SPP, and RPM) and conventional well logging data in order to generate synthetic low-cost RHOB log data
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