Maximizing Information through Data Driven Analytics in Petrophysical Evaluation of Well Logs

Abstract

Modern logging data are characterized by abundance and multiple dimensions: spatial, temporal, and physical. Traditional interpretation workflows for logging data are often limited in their scope and flexibility and use only a subset of the data dimensions, either by choice or necessity. Furthermore, the various deterministic or stochastic workflows typically rely on preconceived rock and fluid models. When those a priori models do not fit the data, confusion can ensue, whereas it is obvious that the correct model is buried somewhere in the "big logging data."To overcome these limitations, new symbiotic approaches using domain expertise and data analytics ("domain-analytics") have been developed. Domain experts use those novel approaches to develop data-driven workflows to explore and mine complex logging datasets for latent but interpretable information. Once validated to be idempotent, the expert workflows that respect both the acquired data and domain knowledge can be packaged into new classes of answers products.In this paper, the novel approaches, along with the examples of new answer products, are presented. These include 1) the automated spatial search for common modes and repeated patterns in the single or multi-well nuclear magnetic resonance (NMR) data, 2) the generation of a data-driven fluid model using time-lapse logging data acquired during multiple passes over the same formation, and 3) the creation of interpretive class-based models using definitive core data that could be propagated to continuous log data for a richer petrophysical interpretation. Big Logging DataRock formations are discontinuous, heterogeneous, anisotropic media resulting from a long and often complex geological history. To understand and quantify the underlying rock matrix and fluid distribution, petrophysicists derive various quantities of interest (Q) such as lithology, pore-size distribution, fluid characteristics, and volumetrics from a number of log measurements (M), as shown in Fig. 1. Those log measurements (M) are the direct transform (g) of acquired physical data (A), whereas the transforms (h) of subsurface quantities from log measurements are mostly empirical and/or model based.Transforms between log measurements and petrophysical quantities are not only difficult to establish but are also limited in scope. Difficulty lies in the fact that most of these relationships are empirical, created by fitting observed results and measured properties in laboratories using cores and fluid samples.

Role of Triphala on Gut Microbiota in the Treatment of Obesity and its Complications

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