The Digital Field initiative is transforming the daily operations on the oilfield and it is now part of PETRONAS corporate wide digital strategy. This transformation is done by onboarding multiple disciplines such as subsurface team, facilities, and operations, HSSE and Business Planning and is designed to replicates the performance of an oilfield in the computer, combining business process management and technical workflows. Digital Field has enabled the customer to execute their work collaboratively, by providing decision support system (technical workflows and business process management tools) subsequently improving their process efficiencies and optimizing their production. It is believed that by conducting a systematic review of the improvement and tracking the values that has been achieved, it will help to promote and accelerate digital adoption faster. The main objectives of the Automated Value Tracking are: To promote opportunity generation through collaborative environment. To track stages of every opportunity of the following categories of Production Optimization, Unplanned Deferment and Process Cycle Efficiencies. To quantify values associated with opportunities generated from automated workflows and current business process. To promote ownership of the actions associated with the assigned opportunity and to help to quantify on individual level contribution to the corporate goal in terms of production volume and time savings. To measures optimized number of opportunities generated against production volume associated with Production Optimization activity according to Field category and Quality of Opportunity Generated from Optimization Advisor. The process is summarized as follow: Opportunity generation: Automated opportunity generation generated through current Production Optimization Advisory framework. Integration with existing Petronas business process tools i.e. Daily Operational Tracking System, Alpha projects, Opportunity Management system, etc. Manual opportunity generation. Opportunity evaluation and analysis: Provide quantitatively confidence level of production incremental volume from automated Optimization Advisory through machine learning. Establish relationship between numbers of opportunity completed and categories versus production volume gain. Opportunity tracking and approval: Tracking the opportunity generation according to the process level. Escalating Opportunity and value recognition through business process approval. This workflow helps to improve to understand the current update of the different levels such as well, field, region and upstream with the help of integrating the value realization and allows "cards" to show information that can trigger opportunities to increase production, reduce time of decision and fast action.

This paper presents a process flow of an efficient method to diagnose, identify and optimize non-optimum gas lifted wells for a gas-lifted oil field. During normal day-to-day operations, a Petroleum Engineer's deliverables include well lifting performance analysis as well as optimization. Gas lifted wells are deemed optimized when the injection point is at the deepest mandrel possible and no multi-pointing scenario is occurring. However, the real-life well production can be unstable, corroborated by real-time data of flowing tubing-head pressure, casing head pressure and gas lift injection rate. The analysis of these real-time data can infer non-optimized lifting, where one of the symptoms is valve chatter. This in turn translates to well slugging and affects the whole production system. In a field with a large number of gas lifted wells, the diagnosis of these types of cases requires a substantial investment of time each month. By utilizing this workflow, the time spent for lifting diagnostics can be reduced, as well as contributing to time savings for other meaningful tasks, i.e. designing new gas lift system on non-optimized wells. The Gas Lift Diagnostic workflow, which is developed using pre-defined logic and the existing well model, eliminates the time-consuming manual task that slows down work and streamlines the processes to generate more value for the business and organization.

PETRONAS Upstream is cognizant of the need to provide a unified digital production platform to the entire upstream community. The digital platform enables the community to perform analytical, collaborative monitoring & surveillance, and optimization to sustain production and improve our operations. Digital Fields (DF) is a modern digital platform, integrating data and analytical services, that provides smarter insights and shed light on potential insights that contribute to better-informed decision-making and improves the way of working. A big data ecosystem and strong infrastructure is the foundation of this digital platform, allied to existing business processes, it allows frictionless secure flow of data, high performance, scalability to support business operations. Digital Fields platform provides the following features: Scalable to all PETRONAS fields operated in Malaysia as well as International assets Solution architecture that allows fast implementation of new solutions and insights A common company-wide platform with the same familiar user interface and user experience The critical aspect to liven up a digital production platform is to identify first the available data sources. Some fields have the luxury of transmitters and sensors installed at the well head, topside facilities and export pipelines where the data can be easily retrieved from the SCADA system for data acquisition. Some other fields with less real-time data luxury would keep their operational data inside some operational reporting documents. A smart report ingestion solution was developed with the means to transform unstructured data from the operational reports, which empowers the users with multiple options for data upload and consumption while ensuring a single, traceable and auditable source of truth. Digital Fields leverages the combination of Daily Operation Reports (DOR), data with different frequency, such as real-time data, with engineering models changing the way the users consume data and provides proactive actionable insights to accelerate tangible values for the business organization. The solution establishes the foundational digital capabilities for field operations and speeds up data-driven value opportunities from operational data and analytics at scale.

The oil industry is evolving and inventing new ways to increase production and spending less time screening candidates. We expanded on the previous paper (Abu Bakar et al. 2020) where we described the benefit of combining multiple workflows based on engineering models to be used as validations checks and created a confidence score for the wells to be executed in the field. The main objective of this paper is to share an alternative solution to enhance or complement the results of each of the workflows by using artificial intelligence/machine learning (AI/ML) to predict well production and/or fill data gaps needed to run the workflows. The workflows taken into consideration in this paper are gas lift optimization (GLO), gas lift diagnostics (GLD), gas lift surveillance (GLS), sand management monitoring (SMM) workflow, and well test validation (WTV).

The oil and gas industry has evolved and embarked into the new era, it includes digitalization and cognitive computing, cyber-physical systems, and cloud computing or so-called industry revolution 4.0 or IR 4.0. This industry has been experimenting with artificial intelligence on any task that is time-consuming, requires precise results, and minimizes human error. Most of the giant operators have difficulty managing huge amounts of data that stream from the field every second. Thus, having a good workflow or system that automates the process, reliable data set, and the right people mindset is a huge game changer. These three main components are the main ingredient for digital transformation. Nevertheless, transforming the right people mindset is key for this objective to be obtained and sustained for long run. Main operator in Malaysia has urge for going digital as part of their transformation journey. By means of collaboration and pace, any operation process or standard operating procedure (SOP) should be reviewed and align with their transformation objective. Nonetheless, managing gigantic data with diverse types and sources from 43 fields is bizarre. This requires a careful plan that consumes lots of resources and effort. Bringing additional barrels with minimal cost and less effort is the key component of digital field objective. Gas lift optimization workflow is introduced as a workflow that automates the process of optimum gas lift injection rate for maximizing the production using real time data. Gas lifts are being re-distributed across all active producers based on the availability of gas lift supply to identify opportunities to improve production behavior at field level. Consecutively to accomplish this objective, a special project team is established for initiation, planning, assessment, evaluation, development, deployment, implement and lastly suitability of the digital ecosystem. Through this journey, few challenges were encountered especially when most of the fields are matured fields with lack of real time technologies and some still in analog mode. For marginal field to invest on such technologies will cost them enormous budget and not feasible for the long run. The objective of this paper will detail out the challenges raised during the project implementation. Some methodologies and strategies of how to overcome the respected challenges will be described further. This paper should be beneficial to all engineers and technologists, especially main operators that plan to take the challenges for digital transformation.