As the field matures and overall field production decline, an accelerated and advanced selection of existing wells for workovers and sidetracks would be critical to meet with the increasing demand for production. Traditionally, an intensive effort is required to identify the right candidates and to ensure the technical and economic success of well interventions, infill-drilling locations and sidetrack locations. An advanced workflow is developed to automate the repetitive and less added value tasks such as data gathering and validation. The data set included historical production performance, static and dynamic reservoir and fluid properties, events and issues encountered within the evaluated wells and regions. The proposed solution allowed an integrated assessment of production enhancement opportunities through various consistent analytic computations, as well as machine learning techniques including Bayesian networks and time-series forecasting models. The automated process generates a comprehensive list of future well interventions including sidetrack candidates, infill-drilling locations and behind casing opportunities with an advanced scoring system and several technical (production performance and reserves) and financial KPIs (i.e. net present value and unit technical cost). Several dashboards built and adjusted with the involvement of various company departments. Lastly, highly ranked opportunities are incorporated into the business plan in accordance to field development targets as well as the availability technical resources (rigs, materials, well availability). The developed solution was tested and validated in a giant mature carbonate field with over 700 well strings in Offshore Abu Dhabi. The solution identified 20 times more feasible opportunities than the typical multidisciplinary team review in 75% less time duration. The automated workflows considered re-evaluating and selecting prime candidates with a reduced risk of failure, therefore improving the technical and economic value by 34%. The workflows are scheduled on daily basis giving a time-dependent assessment and expert monitoring system, which can also notify the operator when problems encountered. Instead of computationally heavy traditional numerical simulation models, the assessment of a large number of well count can be done within hours instead of months. The combination of physics and machine learning based models lead to the development of automated workflows to rank and determine the best candidates via a successful cost optimization and production enhancement.

This paper illustrates strategies, methods, and techniques that have been successfully utilized to model and deploy collaborative solutions to help teams and individuals within and across companies execute business processes efficiently and consistently, whilst ensuring adherence to norms, standards and agreed guidelines for the benefits of shareholders and contractual parties. Cases are presented related to Integrated Reservoir Management, E&P Technical Information Management, and Surface Rights Management for O&G operations. Whilst some contextual aspects of information technology are mentioned in this paper, the focus rather lies in the aspects of value delivery through process governance, workflow automation efficiencies, process improvement, and reduction of time to learn how things must be done on a day to day basis by newcomers. Business process management (BPM) is a technique that brings efficiency and effectiveness to the execution of processes. Rather than replacing big systems, companies are looking at technologies that facilitate implementing operating processes on a workflow orchestration platform that establishes a controlled environment to execute defined activities efficiently. The convergence of business process modeling, business rules engines, process performance monitoring and human workflows has led to adoption of integrated systems leveraging BPM platform solutions. Wide market availability of BPM platform solutions makes possible chosing from diverse types of architecture, functionality, usability and integration capabilities to meet the information management requirements of an operating company. However, adherence to Business Process Modelling Notation (BPMN) and to data and integration standards, is critical to balance governance of processes with agility and flexibility to adapt workflows, especially in companies that require to scale-up workflows initially created for the needs of one functional or organizational area across and into others. Using standards is critical in cases where collaborative workflows require integration and interaction with data, calculations, and transactions performed in existing back office and/or Petrotechnical systems. Successful BPM implementations are those that become adopted by business users and create incremental value. This requires a systematic approach to process improvement opportunity identification, creating realistic business cases, defining and tracking process performance metrics, communicating value and strategic alignment lead by management, and effective management of change.

Meeting energy demands and generating profit to shareholders is a continuous quest for oil and gas companies. Production and business planning in integrated oil and gas operating companies is a complex process involving numerous organizations, historic data collection, modeling, prediction, and forecasting. Integrated business planning complexity intensifies due to the uncertain nature of past facts and future conditions. We propose a framework for integrating upstream and downstream production planning processes using data-driven models representing the upstream capacities, downstream processes, and a countrywide profit model. The upstream production model forecasts optimum capacity scenarios of the reservoir fluids with their compositional characteristics and hydraulic performance of the surface facilities while honoring business rules, and based on the various long-term expenditure scenarios, downtime requirements, and downstream demand schedules. An integrated optimization model for value chain has the potential to protect profitability for oil and gas companies in times of unbalanced market forces.

Efficient reservoir management is defined by its workflow structure stretching from a comprehensive integrated reservoir characterization (static and dynamic) to fast and reliable decision making for optimal field development planning. A lengthy and fragmented process that is scattered among multiple disciplines and data domains. The oil industry has made tremendous effort in advancing technologies to enable faster and smarter subsurface modeling, history matching, scenario prediction and risk analyses. Achieving optimality with reduced-risk requires typically prohibited high amount of human effort and technology resources. As we enter into the Fourth Industrial Revolution of Big Data, Artificial Intelligence (AI) and Internet of Things (IOT) devices, alternatives complimentary tools to the conventional techniques have surfaced. The purpose of this work is to demonstrate the advantages and shortfalls of commercially available data-driven model techniques for dynamic reservoir forecast as compared to traditional numeric simulation techniques. In this paper, a consistent procedure to compare the performance of both data-driven models and full-physics numerical models is developed and discussed. The procedure was tested on an onshore reservoir located in Abu Dhabi, UAE that provided the basis and results for the analysis. The approach included analysis of human resources requirements, computer hardware, simulation performance and models updating strategy.

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.