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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.

Seismic well tie is a critical process to verify the time-depth relationship of a well. This process requires density and sonic transit time data. However, sonic logs are usually not acquired due to cost saving, unfavorable well path, or other operational issues. Attempts to generate synthetic logs by Gardner equation, porosity correlation, or depth correlation did not provide the required accuracy. Therefore, the goal of our project was to generate synthetic sonic logs using machine learning technique for seismic well ties. This paper will compare the different methods tested, compare the results and lists the advantages of using Machine Learning. This approach uses machine learning technique to create synthetic sonic logs. The machine learning model is trained to predict sonic log from other relevant logs. The model representativeness is confirmed by blind tests, which consists of two steps. The first step compares the synthetic sonic logs to the actual sonic logs. In the second step, four synthetic seismograms are generated from actual sonic, machine learning synthetic sonic, Gardner predicted sonic, and averaged constant sonic. The seismic well ties are compared between those four synthetic seismograms. Once the machine learning synthetic and actual logs show similar results, the model is deemed good and can be applied on wells that do not have sonic logs. The synthetic seismograms are then generated using synthetic sonic logs for all the wells that do not have actual sonic logs. The use of synthetic sonic logs gives us the ability to Generate synthetic seismogram to tie wells that do not have sonic dataReduce the number sonic data acquisition, saving time and moneyReduce the risk of long logging string getting stuck in the hole that would requires fishing operations and its associated cost.

Greater Bongkot North is a gas field located in Gulf of Thailand and on production since 1993. Most of the old wellhead platforms (30%) lack remote well test facilities which requires personnel visits for any well test measurement. Often, well testing in these platforms get lower priority compared to other operations in a matured field. This project implemented artificial intelligent (AI) technique to estimate gas rate from other available engineering and geological parameters. A new approach using machine learning was applied to estimate gas production rate where actual measurements are not available. Actual production well test data was used to train the model. Input parameters used were: Surface facility information Fluid properties Production condition Geological setup A blind test on the subset of historical data showed a level of confidence (R2) value of 0.93. This provided confidence to proceed with a full field pilot. A pilot was conducted during January to May 2018. The area of pilot was spread across various geological, operating and surface condition setups to reduce sampling bias. The pilot demonstrated the following use cases: Improved prediction accuracy in wells with no recent test, achieving primary object of model. Detection of well behavior changes: The model could detect changes in well behavior without human intervention much before the trends become obvious for engineers to detect. Improved potential estimation in wells with leaks in wellhead chokes where conventional analysis followed in Bongkot is not possible due to improper wellhead shut-in pressure measurement. Improved efficiency with production allocation: The conventional method requires significant time (40-80 person hours per month) to make the data available for production allocation. This can be shortened significantly by use of this method In essence, this project demonstrated the potential use of artificial intelligent to improve efficiency in a matured gas field operating under marginal conditions.