Shale Descriptive Analytics; Which Parameters are Controlling Production in Shale
Authors: Mohaghegh Shahab D. 
Published: September 2019

Abstract
Descriptive Analytics is the first step of a three-step data-driven analytics workflow used for managing and optimizing completion, production and recovery of shale wells. The comprehensive data-driven analytics workflow for the unconventional resources is called Shale Analytics (Mohaghegh 2017). The key behind Shale Analytics is the incorporation of all field measurements that contribute to the productivity of shale wells. There are workflows in the market that claim to be data analytics related but do not make use of all the available field measurements when performing their analyses. These workflows are mainly based on traditional statistical algorithms rather than Artificial Intelligence and Machine Learning. Such approaches represent different versions of Decline Curve Analysis.
Shale Descriptive Analytics takes into account seven categories of field measurements; (i) well construction and trajectory, (ii) well spacing and stacking, (iii) reservoir characteristics, (iv) completion design, (v) hydraulic fracturing implementation, (vi) operational constraints, and (vii) well productivity. Each of the above categories of field measurements include several parameters. Shale Descriptive Analytics provides two types of insight on the contribution of all the field measurements to well productivity. The first type of insight compares and quantifies the contribution of the different categories of field measurements to well productivity. The second, more detail type of insight compares and quantifies the contribution of each of the parameters of the first six categories to the final category that is well productivity and then compares all the parameters to one another. The Shale Descriptive Analytics presented in this article demonstrate the results of more than 800 shale wells in one of the most productive shale plays in Texas.
Two conclusions have been achieved as the result of this study. (a) In the early life of a shale asset, when the wells are NOT too close to one another (when Frac-Hit is not an issue), using well productivity indices (such as initial production, initial decline rate, first 30, 60, 90, 120, 180 and 365 days of cumulative production, etc.) can provide realistic insight for completion optimization, well productivity and recovery. (b) Once the number of wells in a given asset increases, resulting in the reduction of the distances between parent and child wells (Frac-Hit impacts production and recovery), well productivity indices will no longer be able to provide the required insight for modeling and analysis of field measurements. This is because as the number of wells increases in a given shale asset, the fracture-driven interaction between wells (also known as Frac-Hit) takes over the overall productivity of all the wells in the field. Frac-Hit not only negatively influences parent and child wells productivity and recovery, it completely undermines all the existing techniques (traditional techniques such as RTA and numerical simulation as well as all the existing techniques based on Data Analytics) for completion and production optimization of shale wells. At the conclusion of this article, a new approach to overcome this specific problem is introduced.