Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore

Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and rank the inactive strings, well conversion to gas lift candidates & underperforming candidates resulting into successful cost optimization and production gain.

MSE Based Drilling Optimizer Project for Large National Drilling Contractor

A large GCC National Drilling Contractor is planning to trial a new MSE (Mechanical Specific Energy) Drilling advisory system based in artificial intelligence (AI) on a conventional drilling rig. This system works by means of calculating the optimal auto driller input parameters to achieve higher drilling efficiency. The objective of the MSE based Drilling Optimization system is to drive drilling efficiency by way of advising surface drilling parameters (WOB and RPM). The system will be tuned to advise/automatically modify WOB and RPM for each specific run, section, or well. The parameters can be adjusted in one of the two following ways: Advisory Mode: A recommended WOB and RPM value is sent to the driller, who then manually applies the setpoint change Control Mode: The setpoints are sent to the automatic driller for instant and automated application The system shall enable reduction of NPT and rig days to drill wells by increasing efficiency with consequent cost reduction efficiency by utilizing advanced elements of Artificial Intelligence (AI).

Failure of the semi-authoritarian system: Losing ways of absorbing public voices into the post-handover Hong Kong government

In the past decade, Hong Kong has undergone various large-scale protests, such as the 2014 Occupy Central and the 2019 Anti-Extradition Protests. One of the reasons for such popular grievance was that the government could not grasp the change in public sentiment and opinion. Before the handover, although the governor held the centralized power, the colonial authorities still had ways to collect public opinions to avoid departing from the citizens’ views. The model was called the ‘administrative absorption of politics’. The Chinese authorities attempted to preserve the original advisory system to depoliticize the policy-making process after the handover. This article contributes to the understanding of the development of the cooptation system in Hong Kong and its failure in the 2010s based on the insights of legislators. It also highlights the importance of participation and salient control in the cooptation system to balance public views in a semi-authoritarian society.

A Method of Optimizing a Set of Programs for Mitigating Threats Related to the Undertaking of a Contract for the Execution of Construction Works

This article deals with the problem of limiting the risk of taking up a construction contract for the execution of construction works. The authors have developed an advisory system that will support the analysis of threats on the basis of existing experiences for a specific activity without having to construct an individualized organizational model of an investment. In order to identify a relatively complete set of threats that occur in investment and construction processes in road construction investments, as well as to identify possible programs of their reduction, a model and a method of optimizing programs for reducing risks related to contracts was developed. Threats are considered to be possible events that take place during the preparation, implementation and settlement of any contract. The programs concern specific actions that can be taken in relation to specific threats. Every program contains a set of threats that will be limited as a result of its execution and also has a specific implementation cost. The aim of the proposed optimization is to determine, with regard to costs, a combination of risk reduction programs that is appropriate for the risk states that are accepted by a decision maker. The problem is solved using graph theory and a minimum cover determination algorithm with the use of the minimum alternative formula (mfa) of the Boolean function. A method of actively responding to identified threats during the implementation of a construction contract should take the form of an advisory system that will provide an answer as to what risks should be taken into account when undertaking a contract, as well as what actions can be taken to reduce these risks.

Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

AGRICULTURE IN RURAL AREAS - CHANGES, IMPACTS AND DEVELOPMENT

For two programming periods, Bulgarian agriculture has been developing in the conditions of our common and national agricultural policies. Adaptation processes have led to significant economic, social and environmental changes in farms and rural areas. The aim of the article is to assess the changes in the agricultural sector and their impacts on rural development. The analysis of changes in production and organizational structures and the impacts of rural development are assessed on the basis of statistical information and expert assessment of 163 specialists from regional directorates of Agriculture, municipal services and regional services of the National Agricultural Advisory System. The conclusions confirmed the upward development of Bulgarian agriculture, but with significant structural disparities and different in direction and strength impacts by regions of the country.