ADNOC is continuously enhancing its capabilities to manage its oil and fields efficiently by better planning, execution and operations that drives field development decisions, well performance, and safe operations. In this regard, ADNOC envisages to leverage the evolving Oil and Gas 4.0 technologies to enhance the well planning decisions of the sub-surface and drilling team through data-driven and AI methods. Effective well planning and operations require collaboration between different subsurface teams and drilling team leveraging multidisciplinary data, historical events and risks and constructing integrated drilling and sub-surface model for collaborative planning and keeping the model live. This requires having a live sub-surface model that is kept close to the field reality while reducing uncertainties. However, extracting key learnings, knowledge and experience from a variety of sources and reports is intense and requires lot of manual processing of data. An AI-based solution leveraging data analytics, natural language processing and machine learning algorithms is developed to automatically extract knowledge from a variety of data sources and unstructured data in building a live intelligent model that enables effective well planning, predicting operational hazards and plan mitigation. The solution systematically extracts, collects, validates, integrates, and processes a variety of data in different formats such as well trajectory, completion, historical events, risk offset well information, petrophysical data, geo-mechanical data, and technical reports. Newly acquired data comprising drilling events, geological and reservoir properties are integrated continuously to keep the model live and digital representation.
Low oil prices over the past few years have led oil and gas organizations to embrace digital transformation to improve the efficiency and quality of well design. Nowadays, digital transformation and reducing well construction can have significant impact on determining the financial viability of a discovery. Teamwork and the collaboration of numerous experts in different disciplines are required to achieve a properly engineered well design that can be executed with minimal risks. Present-day well planning uses several increasingly obsolete techniques, including maintaining spreadsheets as risk registers, sharing multiple design iterations between disciplines, drilling, and geological concerns, manually replanning to accommodate changes, and people working in "silos"—often from different offices or from home. Along with this, planning software, when installed on a computer, may be difficult to maintain and update with new software releases. Often an expert in one discipline will not have visibility on the work done by other engineers, impeding collaboration. Many linear processes lead not only to increased well planning time but also to suboptimal well design resulting in higher planning and execution costs (Bello et al. 2014). Today, the oil and gas industry lag in the adoption of digital technologies and most of the ones that exist are not used commercially. Often, the reason is that these solutions address only a very small part of the entire well construction workflow. They are not fully matured, have a poor user interface, and require heavy computing power. Operators, on the other hand, are looking for a full-suite solution and they do not have the resources or expertise to connect various digital solutions existing in the market from different vendors. The need of the hour is an integrated solution that is easy to adopt, provides a collaborative work environment, is cloud-based to leverage computing power (Tanaka et al. 2018), and most importantly supports all the major well design workflows. In this paper, we discuss the first commercial application of a digital cloud-based well-planning solution in the Middle East region, which enabled Crescent Petroleum to become the first operator to adopt the system in UAE.
The oil industry, in its constant strive to maximize gains out of operational data is constantly exploring new horizons where to combine the latest advances in data science and digitalization, into the areas where key decisions to drive economical and operational decisions reside with an aim at optimizing the capital expenditure through sound decision making. High volume operational data has been recognized as hiding many opportunities where the captured details these repositories that include real time logs and bit run summaries, provide a clear opportunity where to extract insights to support optimized decisions in terms of equipment selection to achieve the desired operational objectives. Current possibilities within data science have opened the possibilities through viable solutions, which in this case, aims at providing advise on which equipment in terms of BHA and Bits to select, that would yield the desired outcome for a drilling run. The whole exercise being based on evidence gathered from previous runs where the details for the equipment, the relevant well characteristics, and the observed rates of penetration and the used parameters, are taken into consideration to provide the optimum combination to be implemented in new runs. The present study describes the methodology in terms of data utilization, data science method development and solution deployment, with the associated issues that had to be addressed in order to provide a viable solution in terms of data utilization, technical validity and final user utilization, as well as a series of recommendations to be addressed within any such endeavors to assure the value addition.
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