Complexities in petrophysical and compositional properties as well as significant spatial heterogeneity of rock properties make formation evaluation challenging in organic-rich mudrocks. Conventional methods often overlook the importance of integrated rock classification for evaluation of formation properties, resulting in high uncertainties in estimates of mineralogy, porosity, fluid saturations, and total organic carbon content (TOC). The objectives of this paper include (a) developing an iterative workflow to simultaneously enhance formation evaluation and rock classification, (b) using the estimates of petrophysical, compositional, geochemical, and mechanical properties for completion-oriented rock classification to improve production decisions, and (c) using field-scale geostatistical analysis to extend the introduced workflow to neighboring wells without core measurements and minimizing model calibration efforts, while maintaining reliable formation evaluation results.

First, we perform a joint inversion of well logs for depth-by-depth estimation of volumetric concentrations of minerals, porosity, TOC, mechanical properties, and fluid saturations by integrating information about thermal maturity and core/well-log measurements. These initial estimates are used for a preliminary petrophysical rock classification. Model parameters are updated in each rock class and are used in the second iteration for a class-by-class-based assessment of petrophysical, compositional, and mechanical properties. Spatial geostatistical analysis of formation properties is then used to select the range of neighboring wells where the developed models in each rock type is reliable. This iterative procedure is repeated until convergence of petrophysical/compositional properties in two subsequent iterations or agreement with core measurements (if available) is achieved. Finally, we perform an integrated completion-oriented rock classification to determine the best rock types for completion.

We successfully applied the proposed workflow to more than 100 wells in 20 different counties in the Midland Basin, 7 of which contained core and geochemical data. Results showed that the iterative workflow significantly improved estimates of TOC, porosity, and water saturation by approximately 56%, 28%, and 53% respectively, compared to a conventional method. Results also confirmed that the proposed workflow significantly enhances the formation evaluation and enables reliable reservoir characterization and completion decisions in organic-rich mudrocks. Integrated geostatistical analysis, rock classification, and the advanced iterative formation evaluation workflow, is a novel approach which enables (a) reliable application of the developed rock physics models to wells with no core data or ECS logs and (b) incorporation of spatial heterogeneity of the formation for a reliable field-scale reservoir characterization.

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