In ADNOC Oil and Gas 4.0 mission, we are committed to empower people with the needed capabilities and Artificial Intelligence (AI) technologies to fuel innovation, efficiency and more importantly achieve and sustain a 100% HSE, by transforming the way of handling HSE events by moving from reactive to proactive approach. The ultimate objective is to save lives, empower the vessel Captains to immediately identify and respond to violators, improve the HSE culture of the crew, and automatically generate live data analytics and statistics with the aim of improving safety in operations. The implemented AI use cases are; deviation for not wearing Protective Safety equipment in designated areas, violation of not utilizing safety passages, alert when no watchman in muster station, alarm when man overboard incident, alarm when man fell on stairs, and live Personnel on board each weather-deck. When introduced the Artificial Intelligence cameras, our marine vessels will adopt a smarter automated response and reporting culture, which will in turn, lead to increased safety oversight of our critical offshore operations. Therefore, with the advent of the AI technology, many common business processes have been automated thus enabling personnel to increase their focus on more important tasks while technologies like the AI System can handle many of the time consuming tasks. The solution components consists of Artificial Intelligence platform, high definition cameras, local server, wide-range WiFi access point, network infrastructure and a tablet. On the tablet device, the captain have full coverage of the vessel weather decks, working areas and restricted zones with a feature to generate alerts when detecting an emergency situation. This was provided to empower the vessel Captain to acknowledge and respond to violations as well as take a proactive action to prevent incidents from happening. The Machine Learning algorithm has been trained on actual scenarios and will be continuously improved by adding more recorded event to retrain the initial model. Currently, the prediction model is performing on the vessel operation mode and recording events with high rate of accuracy. In case of automatically detecting an alerting or non-compliance event, the captain would be notified, beacon lights and sound, and log recorded in the local and central system with a photo and a short video clip of the incident. The process of identifying HSE deviations are becoming digitally transformed by deploying AI capabilities on real-time video streams. The AI-based camera system leverages Computer Vision features that enables machines to get and analyze visual information and take action. The whole process of identifying HSE violation events has been digitally transformed by deploying an artificial intelligence solution to perform real time video analytics.

The value calculation for a new digital and innovative technology is often requested by the executive management to justify the cost required for the implementation and maintenance of the technology. The value is normally segregated into a tangible and intangible value that correspond to a quantitative and qualitative description of those value elements. As part of the Digital Oilfield (DOF) assessment, the solution value has been defined using two approaches. Firstly, qualitative value is described using a "FEATURE_BENEFIT_VALUE" model. The qualitative value elements have been grouped to align with the company strategic pillars to achieve its vision. Secondly, the quantitative value has been estimated using an NPV model. It estimates the value of the complete digital solution (combined investment for all domains) being proposed for the Asset. The model estimates the net present value (NPV) of the expected Asset investment in digital enablement and digital capability as defined in the assessment report. Net cash flow graphs are also calculated. The approach used is to calculate an NPV for a GO-NOGO decision. Therefore, a conservative estimate of NPV is made with the mind-set that if even being conservative, the NPV clears the company's hurdle rate for such projects, then the decision to invest is undertaken. Sensitivity analysis has been performed using conservative estimates of production gain enabled by digital and conservative oil prices. The paper will detail out the approach for the value quantification of a DOF solution that will also correspond to the industry guidance. Example on how the value is calculated will also be outlined.

A new integrated growth strategy of an oil & gas company is focusing on maximizing the value of reserves and production in order to meet the value proposition of the highest possible return to the company. The strategy is built on the strategic foundation of the company of People, Performance, Profitability and Efficiency. From a business performance perspective, the strategy will bear fruit through increased production capacity, improved operational and cost efficiencies, re-energizing mature fields and uncovering new resources whilst maintaining safety and asset integrity. The objective of this global level exercise aims to assess and evaluate various Digital Oilfield (DOF) practices and initiatives against industry best practices, to perform a landscape assessment of the upstream assets, to review the asset digital gap, to develop a strategic framework and roadmap ensuring that the company strategic pillars are supported across all relevant aspects, by closing the digital gap between current and future states. The assessment scope covers the following domains: Reservoir management Production optimisation Operation management & integrity Engineering & projects Drilling Efficiency Logistics & Planning The landscape assessment and gap analysis consist of several stages that starts from documenting the information received from the assets capturing their current business practices and processes, analyzing the "as-is" condition, designing the future state, assessing the impact to the specific assets, define the benefits and value and creating a 5-year business roadmap. Aligned with the company DOF strategy, understanding the asset digital gap and enhancing the asset digital maturity will improve: HSE and asset integrity by reducing hazard exposure, optimizing energy usage and improving wells and facilities integrity Collaboration and faster analysis leading to timely decision making Integrated operations by optimized drilling planning, operations, optimized production forecasting and integrated planning Optimum Reservoir Management through enhanced reservoir surveillance and recovery

Collaborative Working Environments (CWE) are a business solution that improve the quality and speed of decision making by enriching the collaboration between teams and individuals, which results in tangible business benefits. The advantages of working in a collaborative environment are well understood in the organization and the concept is widely embraced throughout the petroleum industry. CWEs provide seamless communication between disciplines and between teams in different locations. Traditionally, they have been used to connect staff in remote locations to teams in the headquarters, allowing real time monitoring of the health of the field, and fast decision making on operational issues and short to medium term optimization opportunities. The main goal is to be quickly alerted to events and make smarter, faster decisions using key capabilities available to the company with access to all relevant knowledge, data and analytical tools required to reach a decision. But this drive to make smarter, faster decisions is applicable to all levels of a company. In fact, it becomes increasingly important as more complex decisions are required at higher levels, which can be influenced by interpreted data, personal opinions and perceptions. In line with strategic objective of digital transformation, a national oil company (NOC) has extensive plans to develop asset specific CWEs and enterprise level CWEs. These will be centralized collaboration facilities to provide more rigorous, effective, and consistent surveillance & optimization to help reduce deferment costs and inefficiencies and accelerate decision-making with a measurable business value to enhance HSE, Reservoir, Drilling, Well and Production system performance through emerging digital innovation. All these centers shall be equipped to receive real time and episodic data and perform exception-based surveillance through trending, analysis, and condition diagnosis. All these CWE Centers shall enable decision making with efficient multi-disciplinary collaboration to address business challenges and increase the efficiency of day-to-day operations. They will have clear roles and responsibilities serving as an integral element of the value realization across the assets. The paper will describe the enterprise CWE strategy, key technical considerations, methodology and standards that have been set up to achieve the ultimate objective of the organization to maximize oil field recovery, eliminating non-productive time, enhancing HSE aspects and increasing profitability through the deployment of these various centers.

Abu Dhabi Marine Operating Company (ADMA-OPCO) has made significant progress in its vision to develop Digital Oilfields in their new fields as part of the company's new field development strategy alongside the implementation of new technologies and upgrades in mature Brownfields, as documented in SPE 171713 (ADIPEC 2014) and SPE 137668 (ADIPEC 2010).The building of a historian system infrastructure foundation was the first step toward the automation of dataflow from various field assets to the company's headquarters. This was essential to ensure verified, clean and accurate real time data are provided to users' desktops.The implementation of fit-for-purpose well-centric production workflows helped to realize the different needs of the production and reservoir teams, solving their day-to-day problems and optimizing field production. The workflows included String Status determination, Rate Estimation, Productivity Index Estimation and Well Model integration and Validation as well as Well Integrity Surveillance.A gap though was recognized in delivering an easy to navigate, graphical, interactive, web based view of the combination of real time data, data from the company's in-house developed production data management and accounting system, the corporate petro technical data archive and contextual data. A new visualization tool and modernised automated workflows were therefore developed and implemented on one of the company's fields to address this challenge, the subject of this paper.Current implementation includes a centralized, web-based integrated surveillance tool which is key to manage production performance and gain comprehensive insight into the operations through overall asset surveillance, quick analysis and reporting of key performance indicators including trends of actual production against plan, downtime analysis and well centric production workflows. A scalable, future proofed Field Surveillance Solution (FSS) was implemented. FSS has been built on top of a Production Operations Platform; which provides a single, integrated platform that connects operations for a broad range of disciplines. With cross-domain workflows and integration with other platforms and products, users can see their asset performance in a single environment, regardless of the asset type, size, or location.FSS provided a state-of-the-art platform to visualize the data in an intuitive way simplifying the navigation from a high level overview of all company production to field level, followed by tower level and 2 SPE-183369-MS well level views in detail based on the historian live data and the company's in-house developed production data management and accounting system. This paper details the implementation of integrated field surveillance workflows to facilitate well and reservoir management for production optimization, reducing downtime, and faster decision making to improve overall operational efficiency.