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NOVEL METHODOLOGY FOR AUTOMATION OF BAD WELL LOG DATA IDENTI

Subsurface analysis-driven field development requires quality data as input into the analysis, modeling, 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 pay intervals. As an example, when hydraulic fracturing is used to develop an unconventional reservoir, there is often a demand for elastic rock properties used as inputs into models. The requirement for edited and quality-checked logs becomes crucial as fracturing model inputs span over adjacent shale beds, which are frequently washed out. These washed-out sections may seriously impact logging measurements of interest, such as bulk density, sonic, and neutron porosity. These, in turn, are crucial inputs to fracturing studies, reservoir evaluation, and many machine-learning models employed today. If bad data are fed into a model, then the accuracy and precision of the result can be severely impacted, as per the old adage: garbage in, garbage out. Many machine-learning algorithms employed today do not differentiate between good and bad data. They are also unable to understand the impact of geological variation and influences on the data. Therefore, ensuring that data are of good quality prior to modeling is crucial. 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 multiwell data sets are considered. In this study, a new supervised learning algorithm is presented that uses multiple-linear regression analysis to repair well-log data in an iterative and automated routine. This technique allows outliers to be identified and repaired while improving the efficiency of the log data editing process without compromising accuracy. The algorithm uses sophisticated logic and several solutions derived via singular value decomposition in order to produce regressions to systematically repair various well logs.
Standard price: 10.00
Discounted price: 1.00
10.00
You could save: 90%
Year: 2021
Author(s): Ryan Banas, Andrew McDonald, Tegwyn J. Perkins
Company(s): PetroRes Consulting, Lloyd's Register
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