Integral prediction of shale gas sweet spot based on a novel intelligent method

Summary Shale reservoirs are characterized by its low porosity and permeability, strong heterogeneity and intensive anisotropy. Conventional geophysical methods are far from perfect when it comes to the prediction of shale sweet - spot. Based on algorithms such as fuzzy mathematics, machine learning and multiple regression analysis, an effective workflow is proposed to allow intelligent prediction of sweet - spots location 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, well logging and seismic measurements. 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 allow the quantification of a number of sweet-spot attributes. The practical application of these methods to areas of interest shows high accuracy and resolution of sweet - spot prediction, indicating that it is a good approach for describing the distribution of high quality regions within shale oil and gas reservoirs. Based on these sweet-spot attributes, quantitative characterization of unconventional reservoirs can provide a reliable evaluation of shale reservoir potential.