Enhanced Formation Evaluation in the Permian Basin Using a Novel Field-Scale Workflow Including Wells with Missing Data

Abstract

Summary Reliable formation evaluation in organic-rich mudrocks requires integrated interpretation of well logs and core measurements. More than 80% of the Permian Basin wells have incomplete data sets, lacking photoelectric factor (PEF) or other logs required for reliable formation evaluation in the presence of complex mineralogy. Hence, we develop a novel workflow to reliably estimate rock properties in wells with incomplete data to enhance reservoir characterization and completion decisions. We propose to use integrated rock classification for enhanced physics-based assessment of rock properties in wells with missing data; combine field-scale geostatistical and machine learning methods to reliably reconstruct missing PEF logs with a confidence interval through a rock-type-based approach, which is a unique contribution of this work; and quantify the uncertainty in estimates of petrophysical properties. We performed a preliminary field-scale formation evaluation on wells with triple-combo logs (more than 70 wells). Next, we performed an initial rock typing and reconstructed the missing PEF logs by combining supervised neural networks with geostatistical analysis on a rock-type basis. We then used an unsupervised neural network method to improve the rock classification based on the updated estimates of petrophysical and compositional properties after PEF reconstruction. The combined rock classification and PEF reconstruction was performed iteratively to improve the multimineral analysis results in all wells with missing data. We successfully applied the new workflow to 20 wells in blind tests. The reconstructed well logs agreed with the actual measurements with relative errors of less than 10%. The new workflow extends the boundaries of reliable formation evaluation, enabling accurate reservoir characterization and completion decisions by enhancing evaluation of wells with missing data. The proposed method can also be applied to wells with other types of missing data.