Novel Methodology for Automation of Bad Well Log Data Identification and Repair

Abstract

Subsurface analysis-driven field development requires quality data as input into analysis, modelling, and planning. In the case of many conventional reservoirs, pay intervals are often well consolidated and maintain integrity under drilling and geological stresses providing an ideal logging environment. Consequently, editing well logs is often overlooked or dismissed entirely. Petrophysical analysis however is not always constrained to conventional pay intervals. When developing an unconventional reservoir, pay sections may be comprised of shales. The requirement for edited and quality checked logs becomes crucial to accurately assess storage volumes in place. Edited curves can also serve as inputs to engineering studies, geological and geophysical models, reservoir evaluation, and many machine learning models employed today. As an example, hydraulic fracturing model inputs may span over adjacent shale beds around a target reservoir, which are frequently washed out. These washed out sections may seriously impact logging measurements of interest, such as bulk density and acoustic compressional slowness, which are used to generate elastic properties and compute geomechanical curves. Two classifications of machine learning algorithms for identifying outliers and poor-quality data due to bad hole conditions are discussed: supervised and unsupervised learning. The first allows the expert to train a model from existing and categorized data, whereas unsupervised learning algorithms learn from a collection of unlabeled data. Each classification type has distinct advantages and disadvantages. Identifying outliers and conditioning well logs prior to a petrophysical analysis or machine learning model can be a time-consuming and laborious process, especially when large multi-well datasets are considered. In this study, a new supervised learning algorithm is presented that utilizes multiple-linear regression analysis to repair well log data in an iterative and automated routine. This technique allows outliers to be identified and repaired whilst improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and curve predictions derived via multiple linear regression in order to systematically repair various well logs. A clear improvement in efficiency is observed when the algorithm is compared to other currently used methods. These include manual processing by a petrophysicist and unsupervised outlier detection methods. The algorithm can also be leveraged over multiple wells to produce more generalized predictions. Through a platform created to quickly identify and repair invalid log data, the results are controlled through input and supervision by the user. This methodology is not a direct replacement of an expert interpreter, but complementary by allowing the petrophysicist to leverage computing power, improve consistency, reduce error and improve turnaround time.

Harnessing Advanced Machine-Learning Algorithms for Optimized Data Conditioning and Petrophysical Analysis of Heterogeneous, Thin Reservoirs

“…Petrophysics is a crucial discipline for determining the characteristics of reservoirs and developing new fields. Improved drilling efficiency, data repair, reservoir property prediction, reservoir rock type, and other petrophysics-related tasks have all benefited from the use of ML. , …”

“…Improved drilling efficiency, data repair, reservoir property prediction, reservoir rock type, and other petrophysics-related tasks have all benefited from the use of ML. 3,6 Instead of using traditional approaches, powerful ML is applied to identify petrophysical properties. The optimal model for a precise interpretation of well logs at various well locations is determined using a variety of ML models.…”

“…Within the geoscience domain, the adoption of ML algorithms such as the tree-based approach dates to the early 1960s . Machine-learning applications within petrophysics include the prediction of reservoir properties, , outlier detection, prediction of missing acoustic slowness, and well log repair. ,, Within the petrophysical workflow, data quality assurance is an important step and can take up a significant amount of time within a project. Friedman and Smith identified that poor data quality leads up to a 20% reduction in labor productivity.…”