Distribution of Well Performances in Shale Reservoirs and Their Predictions Using the Concept of Shale Capacity

Abstract

Various empirical observations have been made regarding the nature of the distribution of shale productivity and the potential existence of three dependent intervals that seem to separate the uneconomical wells from the average and good wells. The areas of good well productivity exhibit a log normal distribution and seem to be controlled mainly by the natural fracture system. These differences in performance seem to be related mostly to the shale capacity defined as the product of four key shale drivers: TOC, porosity, brittleness, and fracture density. When drilling a shale reservoir, it appears that the Relative Intercepted Shale Capacity (RISC) seems to have a strong correlation with the resulting relative well performance. Consequently, well productivity could be predicted in a relative sense with reasonable accuracy if the 3D shale capacity model is available to the operator. Having such a 3D model available, a shale operator can compute the RISC of his future wells and adjust their landing zone, azimuth and length accordingly to reduce drilling and fracing costs, to achieve the best return on investment and accelerate the time to payout.

Using geomechanical modeling to quantify the impact of natural fractures on well performance and microseismicity: Application to the Wolfcamp, Permian Basin, Reagan County, Texas

“…When adequate pre-or poststack seismic data are available along with well data, then the 3G workflow benefits from a full-fledged CFM workflow (Ouenes, 2014;Newgord et al, 2015), which incorporates multiple G&G data sets to calculate a more accurate estimate of the 3D fracture density model. Due to limited publicly available well log and seismic data in this study, the CFM uses only the coherency seismic attribute as a proxy for NFs.…”

“…To enhance communications between engineering and G&G staff, NF density and other key geologic factors including total organic carbon, brittleness, and porosity are quantitatively combined to form a new reservoir property called shale capacity (Ouenes, 2014;Newgord et al, 2015), which demonstrates the value of geoscience in predicting the engineer's bottom…”

Estimation of Stimulated Reservoir Volume Using the Concept of Shale Capacity and its Validation with Microseismic and Well Performance: Application to the Marcellus and Haynesville

“…Although these microseismic challenges shall be addressed in the near future, one must currently find another approach which relies on hard geophysical and geologic data to estimate the SRV in a reservoir simulator. introduced the use of the Shale Capacity (Ouenes, 2014) in the computation of the SRV and the estimation of the key properties involved in the SRV. The hard questions which must be addressed when discussing the SRV are: 1) is the enhanced permeability KSRV uniformly distributed in the SRV ?…”

“…Figure 1 shows the Shale Capacity along a poor well and a good well. Details on how the four Shale Capacity drivers are estimated can be found in Ouenes (2014). The Shale Capacity has some striking features such as high Shale Capacity values tend to be in low closure stress zone.…”

“…2 -Where l = Length of the wellbore intercepting positive Shale Capacity -Where L = Total length of the wellbore When using the IP for six Marcellus wells which produced in 2010 and 2011, we find a strong correlation with an R 2 = 0.73 between the Relative Intercepted Shale Capacity (RISC) and the IP as shown in Figure 1. The most interesting part of the use of Shale Capacity and the associated RISC concept is the ability to predict the IP in other wells as described in Ouenes (2014). This ability to predict well performances from the Shale Capacity may provide the geologic information necessary to estimate SRV properties.…”