Big Data Insight towards Well planning, A case study

Abstract

Abstract There are various factors that contribute to the Well planning. Be it cost, complexity of the reservoir, safety measures, isolating problem areas, engineering, etc., The whole process of well analysis takes a lot of time and efforts, as the method used is mostly manual. In this abstract, we explain the Big Data approach used to perform Well Planning which significantly reduces the drilling engineer time. Future, possible suggestions to the existing system are also discussed in this paper. Over the past few years, the concept of Big Data has gradually matured. Drilling Engineers, Data Management and the Solution Development Team have jointly discussed the potential and the functionality if this concept to optimize the Drilling Engineer performance and timeframe for preparing and delivering drilling programs for the new development wells. EDM (Enterprise Drilling Management System), drilling suite of applications, In-House drilling data extraction tool with Web based interface, and many different types of excel sheets, were considered as the input sources. Well planning process was automated for offset and related data acquisition, results and knowledge were captured in a central repository, which will be used in future endaovers. Fig 1. Shows existing architecture and proposed roadmap to develop a robust system for predicting the new drilling well location. For example, all the data related to NPT records for the wells in the delimited area, their reports, and daily drilling records were brought together. Then, a cutoff value was decided and color coding was used to highlight wells near the offset wells. This layer was overlaid on GIS map to see the exact location and on the In-House web based tool. The criticality was highlighted using the color code. Based on the above, this solution aims to enhance the drilling engineer performance and represents a major contribution towards increasing the efficiency, as wells as the improvement of the drilling operations. In Future, the team plans to add more complex algorithms based on refining the search criteria, extracting data for the offset wells and implementing Artificial Neural Network. The team is also working on connecting corporate datastore for the exploration, production, and development data to EDM. As of now, the EDM is connected to Corportate database through views to access Directional surveys for Wells. Bringing all the data types and sources together has made the analysis of offset wells simpler. The process is more automatic and efficient. It has transformed the process from having local copies of the data, copying data from different sources, and manually applying formulas and generating reports into a centralized repository, in addition to dynamic and automatic reporting. It also, introduces better accessibility from different drilling engineers for enhanced communication, collaboration and knowledge sharing.