Data-Analytics-Based Wireline Formation Tester Adaption and Optimization Study, West China Ultradeep Formation

Abstract

Abstract The Kuqa foreland thrust belt, as a secondary tectonic unit of the Tarim basin at the front of the Tianshan Mountains, is a foreland basin that formed in the Late Tertiary. The lower Cretaceous Bashijiqike tight sandstone in the basin is an ultralow-permeability and low-porosity reservoir. The Kuqa foreland thrust belt includes Kela, Keshen, Bozi, Zhongqiu, and Alvart blocks. Although these blocks developed under the same sedimentary conditions, the permeability-porosity relationship and wireline log response can be very different among the blocks. Whereas the shallow zone has been had E&P activities for decades, fully understanding the fluid properties, the porosity-permeability relationship, and distribution pattern of gas in the deep to ultradeep zone is of strategic significance and can provide the experience for the exploration of similar gas reservoirs in China and worldwide. The main target zone depth varies from 6000 m to 8000 m, and the formation pressure is near or exceeds 20,000 psi. Compared to a time-consuming and costly drillstem test (DST) operation, the wireline formation test (WFT) is the most efficient and cost-saving method to confirm hydrocarbon presence. However, the success rate of WFT sampling operations in the deep Kuqa formation is less than 50% overall, mostly due to the formation tightness exceeding the capability of the tools. Therefore, development of an optimized WFT suitable to the formation was critical. More than 30 WFT wells in Kuqa foreland thrust belt were studied to understand the well and formation conditions causing the success or failure of these WFT operations. By doing a statistical analysis of more than 1000 pressure test points, we researched the relationship between mobility and petrophysical logs such as neutron, density, gamma ray, resistivity, P-sonic, etc. Several statistical mathematic methods were applied during this study, including univariate linear regression (ULR), multiple linear regression (MLR), neural network regression analysis (NNA), and decision tree analysis (DTA) methods. A systematic workflow was formed to mine data information, and we delivered a standard chart of the relationship between mobility and the petrophysical logs, an integrated equation based on MLR, and an NNA model that can be applied to WFT feasibility analysis. These methods can be considered the foundation of artificial intelligence (AI), which can be used in future mobility automatic prediction. This provides a rough estimation of the mobility and sampling success rate and enables WFT optimization to be conducted in advance.