Data Analytics in Analogue Benchmarking of Shale Resource Plays: Perspectives for Saudi Arabia Shale plays
Shale plays are relatively large accumulations that occur over a broad geological area where the likelihood of encountering hydrocarbon bearing strata is nearly certain. In addition, they are known to exhibit wide variability in well performance. Such complexity has motivated their consideration as “statistical” plays in which a large number of wells has to be drilled and stimulated as a pre-requisite for understanding what influences performance and repeatability (Schepers et al, 2009; Meehan, 2010; Lee, 2017). There are many opinions and speculations on what happens as we embark upon drilling, completing, and hydraulically fracturing shale wells. Basically, a full understanding of the physics and mechanics of the storage, fracturing and flow phenomena in shale has remained elusive to a large extent (Mohaghegh D. S., 2017). However, as an industry, we have acquired a substantial amount of data which if analyzed could unlock the knowledge needed to optimize production. Emerging plays outside North America are drawing attention and Saudi Arabia is among those pursuing the exploration and appraisal of similar resources (Ahmed Al-Mubarak, 2017). North America’s Eagle Ford shale has been identified as an analogue of some shale plays in the Kingdom. In search of insights on primary drivers of well productivity drivers and the associated statistical variance, this study undertook a retrospective analogue shale plays assessment based on data analytics approaches comprising of detailed information capture, classification, analysis and visualization. Information was captured from public databases archived by Texas Rail Road Commission, FracFocus Chemical Disclosure Registry and published studies. The evolving well productivity trend for the mature Eagle Ford reservoir was profiled by looking at production and stimulation parameters for wells in a similar geological setting. From this study, it was found out that most stimulation parameters correlated positively with cumulative hydrocarbon production. Knowing that correlation might not be causation, further interrogation indicated that multiple parameters such as the amount of proppant and fracturing fluid volume had increased in tandem. Based on technical arguments, ratios such as average proppant concentration and multi-parameter cross plotting were employed for decoupling parametric associations. The resulting good correlations of fracturing fluid volume with cumulative hydrocarbon production was interpreted as an indication of how it was likely to be the most essential driver of the shale plays’ well productivity. Incorporation of simple production data diagnostics supported earlier interpretations. These solutions illustrated that the over-parametrized hydraulic fracturing process creates an illusion of statistical variance in well productivity yet systematic constraining of primary parameters is most likely to yield performance repeatability. As a conclusion, data analytics approaches coupled with a priori knowledge will be useful for improving analysis of shale reservoir multi-stage hydraulically fractured horizontal wells during field appraisal, piloting and development.
AAPG Datapages/Search and Discovery Article #90333©2018 AAPG Middle East Region, Shale Gas Evolution Symposium, Manama, Bahrain, December 11-13, 2018