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This paper deals with the lessons learned during the completion design for a HPHT gas field (Field-X) offshore Sarawak, Malaysia. It is a carbonate reservoir with more than 500 meters of the gas column containing between 6-10 tcf of OGIP making it one of the major discoveries in recent years in this region. However, the combination of HPHT and the presence of about 18% CO2 and 2% H2S in the gas makes it a complex development, particularly for well and completion design.To address the challenges associated with the development of this field, a taskforce was formed with personnel from all relevant disciplines to come up with an appropriate design that can help produce from this reservoir safely. The project is now in the detailed design phase where the final aspects of the completion designs are being formulated. Considering that this will be one of the first development in this area with this combination of reservoir conditions, it is expected that the lessons learned from this project will support other future developments too.The paper discusses lessons learned during the design process specifically related to material selection for the highly sour environment, temperature modeling, identification of annulus pressure envelopes and their management, perforation philosophy, and surveillance methods for both reservoir management and well integrity.Another aspect which affects the overall well and completion philosophy for a complex development is the challenges associated with procurement and component qualification. At this stage, the key issues are being identified and will be briefly covered in this paper.

The Company's digital transformation project was started in 2018 to overcome the challenge from digital disruption, energy transition and hydrocarbon reserves declination. Drilling and well engineering cluster is a key player in operating cost optimization which requires digestion and analysis of tons of data generated through daily operational reports. Thus, several digitalization projects have been initiated to optimize drilling and well process to be better, faster, and safer. In this paper, we intend to share a successful journey of digitalization under drilling and well cluster. The transformation journey started with identifying engineer's pain points in each process, among which the most common were redundant processes, manual data inputs/calculations, and time spent on collecting and analyzing unstructured data sources. Moreover, diversified practices on engineering programs and data analysis adversely affect well design standardization and optimizations. To counter those pain points, digital transformation projects were ideated by using digital solutions and technology which are grouped in 5 focus areas: centralized data platform, business intelligence, robotic process automation, digital assistant, and data analytics from fundamental to advance level respectively. Besides out-of-the-box solutions, many internally developed systems have been utilized throughout the journey which helps engineers to build their digital capability and awareness. There are over twenty (20) drilling and well digital projects implemented since the transformation project has been started. And as a result, engineers’ workload has been reduced significantly using digital solutions such as data extraction, visualization, and in-depth analysis. For example, the first successful project under robotic process automation utilizes text analytic and Artificial Intelligence (AI) techniques to analyze well conditions and integrity status from unstructured data and to generate reports within a limited timeframe. Another successful case is well design automated workflow which saved around 40% planning cycle time and provides a better design quality which will lead to a lower well cost. Total cost saving of over 40 mmusd has been recorded so far under the digital transformation project. Furthermore, there is an outlook for additional cost-saving through future new projects, integration, and scaling up plans across the company's international assets. The results and outcomes are very promising to this point. We believe that these initiatives will help the company to improve productivity, benefits, agility, and move beyond business disruptions. Combination of drilling and well expertise with digital transformation solutions will significantly improve well design process, quality and operational efficiency which make industry stay competitive and resilient in the future.

InSight is the corrosion-wise monitoring tool that aims for facilitating the engineers in input data consolidation and corrosion analysis. By using the Robot Process Automation (RPA) digital tool and Business Intelligent (BI) tools, this time-consuming process will be dramatically expedited. Now, the project scope is only to analyze and monitor field integrity e.g., surface piping and downhole well tubing. However, it can be scaled up to other applications and business areas. RPA on the engineering software requires detailed steps and layers of commands. Developing the new RPA engine is a more flexible and generic way to manipulate the engineering software. The well test data stored in the database was linked to the on-premises server in which the RPA engine and corrosion simulation were deployed and installed. After daily data arrived, the RPA will be automatically run and transfer the result of the corrosion analysis back into the database. BI tool then pulls the data from the database and visualizes the analysis result, also has an alarm function to responded parties. InSight has been developed by PTTEP since 2020. RPA engine for two corrosion simulations was completely deployed namely PRECORR and OLI. Now running corrosion simulation is easy with just one click and over a thousand cases can be run within a day. Generally, this corrosion simulation via RPA is 9 times faster than the human. The engineers will then have more time to focus on data quality verification and the data analytics of the simulation result. The tool is quite flexible for the users. The data pulled from the daily database can be manually selected and corrected. In addition, a sensitivity analysis can be designed. Likewise, the result visualization can be customized. Subsequently, the actual field corrosion logging data or the laboratory corrosion tests can be tied in for the model calibration. Moreover, the tool can add more corrosion simulation software with additional effort. However, the main users are still improving the tool performance throughout the real use cases. This RPA engine will change the world of routine corrosion simulation and monitoring. Also, it can be supplied to any routine engineering simulations. For this project, the well will be produced safely with close monitoring of corrosion and an alarm system.

Interest in CCS project development is accelerating in SE Asia, driven by the need to monetize emission-intensive assets in the region while complying with increasingly ambitious GHG emissions targets. Depleted hydrocarbon fields represent an attractive storage option for early CCS project due the enhanced understanding of the reservoir, its dynamic behavior, and proven storage capability. Re-use of existing infrastructure also presents the potential to reduce both project costs and time to first injection, however, these brownfield sites also carry significant risk to the long-term, safe containment of injected CO2 through risk of leakage via legacy wells. A methodology is presented in this paper to investigate the risk-reward balance of developing a depleted gas field as a storage site in the Gulf of Thailand. A screening process to assess all abandoned, suspended, and active wells is used to identify wells with re-use potential as CO2 injectors or CO2 plume monitoring wells, and those which represent a leakage risk to the project. A set of legacy well risk identifiers is generated for the field based on well construction records, descriptions of current well barriers, well utilization history, and current best practice guidelines. Southeast Asia has significant remaining reserves of oil and gas, and coal, and an active liquefied natural gas (LNG) export industry. The region's energy demand is increasing rapidly and is forecast to continue to grow over the next decades (World Economic Forum, 2019). To date, fossil fuels have supplied nearly 90% of this growth in the demand for energy in the region (IEA, 2021). To meet this growing energy demand, several new gas projects are under development across Southeast Asia, but many of these are associated with high CO2 gas fields where the produced gas contains significant (up to 70% by volume) CO2 (GCCSI, 2020). In Thailand, where nearly 94% of the primary energy is met by fossil fuels (BP Statistical Review, 2022), the energy sector represents the biggest contributor (74% in 2013) to the country's greenhouse gas emissions (GHG; UNFCCC, 2020). However, as per the nationally determined contribution to the United Nations Framework Convention on Climate Change (UNFCCC), Thailand intends to reduce its GHG emissions by at least 20% from projected business as usual levels by the year 2030 (UNFCCC, 2020). Carbon capture and storage (CCS) represents one option to help meet this increased demand in fossil energy while also reducing GHG emissions. An approach which is gaining traction across the region is to utilize the high concentrations of CO2 stripped out of the raw gas streams at gas processing plants and, instead of venting to atmosphere, the CO2 can be compressed, dehydrated, and transported to suitable long-term storage locations. Depleted oil and gas fields form an attractive opportunity for long-term storage of CO2 due to the wealth of both static and dynamic knowledge available from appraisal through production activities. Depleted fields also have the advantage that they have a working primary seal for hydrocarbons, which has been proven over geological time and so can be considered, in general, to carry low risk of leakage through geological means. Brownfield sites can, however, also represent a challenge to project success through an increased risk to the containment of the injected CO2 due to the presence of legacy wells. These existing wells represent a variable risk to containment depending on well age and type, well history, well design, and plug and abandonment methodology applied. This paper presents the outcomes of a CO2 storage feasibility study for a depleted gas-condensate field in the Gulf of Thailand. The main aims of the study were to:1) identify the project risk associated with the integrity of the field legacy wells, and 2) to evaluate the potential for well re-use for the CO2 injection project. Reusing an existing field offers new life to an otherwise end-of-life asset, inching towards decommissioning and site closure. As commercial scale CO2 storage in depleted hydrocarbon fields represents a ‘First of a Kind’ project, the feasibility study is designed to evaluate the current status of the field and surface facilities with respect to CO2 injection and long-term storage. As a feasibility study, the focus of the technical work was to identify any ‘showstoppers’ which might indicate that the selected site was not suitable for long-term CO2 storage and, if sufficient positive storage indicators were identified, to select the most appropriate options for progression into a Concept Selection study in which more detailed engineering studies will be completed.

To ensure all wells are in good and safe condition and can be operated as designed, the well integrity of individual wells must be maintained. However, lack of utilization and analysis of the well integrity status in brownfields with lots of qualitative data, the different well integrity standards, especially in newly acquired assets with unconsolidated well integrity database, and limited resources. The Well Integrity Management project was then proposed, and the Visualization Management Platform (VMP) was created. The VMP will centralize the well integrity database for all operating assets, develop a "Well Failure Model" for better classification of well integrity problems with the right actions to be taken, and develop a visualization of well integrity status using the business intelligent application in Power BI, for mapping well integrity status with other critical related key information i.e. production performance, remaining reserves, and integrity records. It will automatically generate a work plan based on the priority to be executed, well location distance, and weather forecast, automatically generate a work program to reduce the engineer's task, and automatically send an alert notification to the related person to prevent overdue tracking. More than 3,000 wells have been effectively monitored and managed thru this platform with limited resources. The VMP defines and provides the operating standards and guidelines to maintain well integrity, ensuring safe well operations. The system can provide real-time monitoring for better planning and minimizing backlog, better decision making on well utilization management, set the priority for well maintenance, and create an efficient Workplan for well services working team, as well as minimize routine tasks for an engineer in issuing well integrity work program. Moreover, the system will send an auto alert to the responsible person when there is any issue about well integrity to reduce work process cycle time which will reduce the potential risks to well equipment and personnel. The system is also useful for plug and abandonment when wells have come to the end of their lives because it contains information about the history well activities and current well status. Not only do the existing operating asset get benefits from this platform but also the management and the asset team. Digital technologies were used for real-time extracting information from several types of data formats, integrating it into the data lake in Google Cloud Platform, and demonstrating in the Business intelligent tools. This was the showcase to apply the digital tools supporting the brown field's well integrity management to improve the well assurance and make your wells safe for everyday operations.

Beam pump and ESP are common artificial lift techniques in pumping systems. They are widely used as primary oil recovery methods, but system failures lead to production deferments and increases in operating expenses. Employing decades of our field data, promising data science techniques are discussed here to analyze the factors governing failures in both beam pump and ESP approaches. These data are then applied with machine learning models to predict service life, failure mechanism performance, and production deferments. The data analytics process begins with data preparation. Field data were extracted, transformed and loaded into a data warehouse for further processing. These data were categorized by failure information, pump configuration, wellbore geometry, and production information. The significance of each parameter causing pump failures was derived using a process called "Attribute Forward Selection (AFS)." Then several machine learning algorithms were implemented and compared with to determine the most appropriate model to predict pump service life. More suitable pump configurations to improve pump service life were conceptually recommended based on the analysis. Differences in parameter significance was identified by attribute forward selection, and is displayed in a heat map. It was seen that the use of beam pumps in highly tortuous wells received the number one ranking as the main cause of failures whereas sand production was revealed as the most significant parameter relating to ESP failures. Correlations for these parameters were mapped by machine learning algorithms, resulting in multivariate failure prediction models (i.e. involving more than one parameter at a time) to predict the service life of beam pump and ESP systems. For both artificial lift systems, the models with the best correlation found thus far are based on a neural network, which resulted in the highest R-squared values when compared to other techniques. This neural network model was validated with the actual information, and the outcomes using this model are presented via a scatter plot in this paper. The plot shows that the prediction for ESP forms a trend around the theoretical best match line. In contrast, the prediction for Beam pump still needs improvement, with the data being scattered around the straight line with a unity slope. Data science is an emerging technology that recently has provided breakthrough results for big data analysis. This paper will demonstrate the application of such discipline to the area of artificial lift. Machine learning is a promising tool which could help improve human understanding of complex problems, and, in this case, could furnish a durable competitive advantage to the oil and gas industry.