Identifying Leading Indicators to Address the Risk of Drilling and Completions for Shale Gas

Abstract

Abstract A successful drilling and completions operation for Shale Gas is the key to ensure wellbore integrity throughout the life of the well. Drilling for shale gas involves a lot of uncertainty regarding the geology, geography and well geometry (3G's). It is thus imperative to identify the leading indicators for failures to avoid the costly measures later required to solve operational problems and reduce non-productive time. However, the uncertainty factors (3G's) vary for different regions like Marcellus and Barnett shale; as well as for different wells in the same region as seen in the Eagle Ford shale play. Hence it becomes difficult to implement the know-how of one region to another region. This paper proposes a novel framework to identify leading indicators to address the risk in shale gas drilling and completions. The proposed framework is divided into three main parts: (i) collection of past failure data, 3G parameters and real-time monitoring data; (ii) estimation of real-time damage growth using the proposed model that combines physics based failure model and data collected from previous stage; (iii) identification of leading indicators and estimation of reliability parameters and updating those to the “live” databases. The paper includes a case study on casing failures related to shale gas operations. The specific failure mechanism that is often seen in shale operations is brittle longitudinal splits of couplings. This paper discusses the failure analysis of longitudinal splits of P-110 casing. The study then identified the leading indicators based on the failure analysis. The sensitivity studies on variability of initial damage level and loading on the stochastic damage propagation model are also conducted. The sensitivity study results show that the damage propagation is largely influenced by the variability of initial damage level and loading process. This paper also suggests leading indicator mapping process. This includes maintaining a database on mapping of generic failures and their corresponding leading indicators. This enables the analyst to use the data from different regions interchangeably by extending the boundaries of failure analysis.