Total Organic Carbon Predictions from Lower Barnett Shale Well-log Data Applying an Optimized Data Matching Algorithm at
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Pure and Applied Geophysics
Total Organic Carbon Predictions from Lower Barnett Shale Well-log Data Applying an Optimized Data Matching Algorithm at Various Sampling Densities DAVID A. WOOD1 Abstract—Accurately estimating total organic carbon (TOC) from suites of well logs is essential as it is too costly and time consuming to take direct measurements from core samples in many wells. Unfortunately, the several methods developed over recent decades, based on various correlations and correlation-based machine learning methods, do not provide universally reliable, accurate or easily auditable TOC predictions. A method is developed and its viability evaluated exploiting a promising correlationfree, data-matching routine. This is applied to published well-log curves, with supporting mineralogical data and measured TOC, for two wells penetrating the Lower Barnett Shale formation at distinct settings within the Fort Worth Basin (Texas, U.S.). The method combines between 5 and 10 well log features and evaluates, on a supervised learning basis, multiple cases for nine distinct models at data- record-sampling densities ranging from one record for every 0.5 ft to one record for every 0.04 ft. At zoomed-in sampling densities the model achieves TOC prediction accuracies for the models combining data from both wells of (RMSE B 0.3% and R2 C 0.955) for models involving 6 and 10 input variables. It is the models involving six input variables that have the potential to be applied in unsupervised circumstances to predict TOC in surrounding wells lacking measured TOC, but that potential requires confirmation in future multi-well studies. Keywords: Well log TOC estimates, data-matching machine learning, zoomed-data interpolation, correlation-free feature selections, model transparency, data record sample densities.
1. Introduction Since the recognition in the 1940s that oil yield from shales was linked to their Uranium contents which was recorded by gamma-ray (Gr) well logs (Beers 1945; Swanson 1960) there has been intense interest in developing accurate methods to predict organic matter (OM) content and source rock properties from single or multiple well log curves. OM
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has quite distinctive petrophysical properties from the inorganic minerals that dominate sedimentary rocks, as well as its distinctive chemistry dominated by hydrogen (H) and carbon (C). Its lower density and acoustic velocity (higher travel time) and higher resistivity and H content mean that the basic wireline logs, Gr, bulk density (Pb), resistivity (Rs), neutron (Np) and acoustic travel time (DT recorded from a P-sonic log), can all be used to an extent as indicators of OM, total organic carbon (TOC) and other source rock properties (Meyer and Nederlof 1984; Mann 1986; Fertl and Chilingar 1988). The most accurate way to obtain TOC values from profiles through rock formations is through laboratory measurements on samples from well-bore cores, side-wall lateral-cores and drilling cuttings. To obt
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