Study Reviews Eagle Ford Parent-Well Frac-Hit Analysis, Prediction, and Mitigation

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 3873139, “Eagle Ford Parent-Well Frac Hit (Frac-Driven Interaction), Impact Characterization, Prediction, and Mitigation,” by Yongshe Liu, Lucy Luo, and Susan Naiser, ConocoPhillips, et al. The paper has not been peer reviewed. _ While some wells show production uplifts after a frac hit (FH), many wells experience production degradation or complete loss of production because of failed casing or plugged wellbores. The complete paper focuses on Eagle Ford FH characterization, production-impact-prediction methods, and mitigation techniques. The techniques and findings presented in this paper are intended to improve the understanding of FH trends and their effects on the development of unconventional reservoirs. FH Detection and Data Collection Parent-Well Production Data. The authors write that identifying and building a database recording FH effects for every parent well has been a significant effort, with more than 1,600 wells drilled in the Eagle Ford by ConocoPhillips. This mass of data, however, serves as an excellent record of changes to production rate, water cut, yield, and pressures. Parent-Well Surface Pressure. Parent-well surface pressure data can be important for FH analysis. Such data provide direct measurement of the timing and magnitude of FHs at the parent well. FH-event timing typically can be correlated with the pumping of a specific completion stage when integrated with evaluation of stress and expected fracture orientation. With this information, an FH pathway can be inferred between the infill and the parent wellbore. Collating this information assists in the generation of a map that captures all FH events with the extension of this analysis to all stages and parent wells. To characterize FHs, parameters derived from the pressure response include the rate of pressure increase (intensity), pressure-increase magnitude, time to response, and volume to response. An automated tool has been created to identify these pressure-based parameters and record them in a database. FH Diagnostic Data. FHs can be detected using special hydraulic fracture diagnostic data that are not collected commonly, such as microseismic, distributed acoustic sensing, downhole pressure gauge, and tracer data. Other relevant information used for FH analysis includes well spatial configurations, completion data, reservoir properties, and operational data, such as mud-loss and cleanout data. FH Analysis Work Flow The proposed integrated work flow for analyzing FHs involves collecting data on fieldwide FH events and their associated production effects and incorporating other relevant information such as geology, completion, and well schedule. Empirical analysis is conducted to identify the key trends and drivers of FHs, followed by the development of mechanistic models for a better understanding of the underlying physics and a multivariate analysis (MVA) model for prediction.