Abstract
Shale reservoirs are characterized by low porosity and strong anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet spot locations, and even less reliable when attempting to delineate unconventional features of shale oil and gas. Based on some mathematical algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet spots and comprehensive quantitative characterization of shale oil and gas reservoirs. This workflow can effectively combine multi-scale and multi-disciplinary data such as geology, well drilling, logging and seismic data. Following the maximum subordination and attribute optimization principle, we establish a machine learning model by adopting the support vector machine method to arrive at multi-attribute prediction of reservoir sweet spot location. Additionally, multiple regression analysis technology is applied to quantitatively predict a number of sweet spot attributes. The practical application of these methods to areas of interest shows high accuracy of sweet spot prediction, indicating that it is a good approach for describing the distribution of high-quality regions within shale reservoirs. Based on these sweet spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.
Highlights
Shale oil and gas reservoirs are increasingly valued worldwide due to their unique characteristics (Sayers 2005; Vanorio et al 2008)
A typical shale reservoir is known with the following features: high TOC, complex pore space, strong anisotropy, complicated distribution of hydrocarbon, micro-fractures developed and extremely low Edited by Jie Hao & Ke-Ran Qian qiankeran@hotmail.com
Gading et al (2013) pointed out that the description of shale reservoirs may be obtained through the analysis of seismic data, based on the relationship between TOC and rock properties established in petrophysical analysis
Summary
Shale oil and gas reservoirs are increasingly valued worldwide due to their unique characteristics (Sayers 2005; Vanorio et al 2008). Zhang (2012) and Zhang and Sun (2012) started to study the shale oil and gas accumulation process, to allow isolation of the crucial factors controlling shale oil reservoir quality and abundance; they studied core and log data to establish a sweet spot evaluation method based on five indexes, which are the mineral components, geochemistry, reservoir properties, hydrocarbon saturation and fracturing potential. The current approach to sweet spot predictions is mostly based on geochemical analysis of core data and the petrophysical analysis of well logs The deliverables from these studies are one of the key reservoir attributes, either TOC or brittleness, which only describes either the geological or engineering aspect of the shale reservoir sweet spots
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