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 reservoir management team is often facing a standardization challenge during audit and screening of inactive wells, especially if this task involves multiple mature oil reservoirs or fields. Such a well candidate screening process is normally required to select candidates for revival as well as plug and abandonment (P&A) candidates. Shut-in wells across different fields may be sharing common issues such as pressure depletion, liquid loading, and high water cut, however, the severity of well-related problems varies from one field to the other. This is in addition to the variation of wellbore mechanical issues such as well-bore integrity, wellbore accessibility, and others. This paper aims to demonstrate a workflow to provide a quantitative ranking of wells. It can be used to standardize an audit process during multi-reservoir or multi-field inactive-well candidate screening study. The standardization process was addressed by developing a tool that registers the shut-in well ranking upon completing the well potential and risk assessment process. Well level petroleum engineering and production data analysis such as decline curve analysis, nodal analysis and well modeling are performed to estimate the remaining well potential. Subsequently, to enable a comparison across different fields, behind pipe well potential was normalized using multi-field parameters. The audit process followed with well workover risking based on ease of workover intervention including workover options such as water shut off, remedial wellbore integrity work, stimulation and others where it also draws on local knowledge for well risk calculation. The approach presented in this study provides a comprehensive tool for both key performance indicators; remaining well potential and well risk, that are usually required to short-list wells for workovers. The standardized audit process was demonstrated in a case study where a large number of shut-in wells from multiple mature oil fields were ranked. In this study, the 7 highest ranked wells were recognized as production enhancement candidates and conversely, a number of wells with the lowest ranking were identified for well abandonment. Through this standardized workflow, the well risk assessment was performed efficiently with tools that enable a consistent result across different fields. It helped to accelerate the reservoir management decision-making process in identifying wells with the most impact to increase the success probability during inactive well revival and workover. The workflow and the tool presented in this paper has the potential to be used as analytic tool or template and can be used as a live document that may be adopted to reduce the workload and improve shut-in well management.

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.