The successful discovery of petroleum exploration primarily depends on the understanding of the basin evolution and sedimentary filling though geological time. Well data also play a key role for reservoir presence and quality analysis; however, none of well fully penetrated the Oligocene Syn-rift sequence in the West Arthit area. Therefore, this study aims to overcome the challenge of limited well information by performing the Forward Stratigraphic Modeling (FSM) to determine basin evolution, depositional setting, and reservoir distribution in this area. The FSM model is constructed with the inputs of paleo-bathymetry, subsidence, sediment supply, water level, and climatic cycle. In addition, the stratigraphic sequence is reproduced based on field observations such as rock samples, seismic mapping, well-log responses, and publications from nearby areas. The main uncertainty of building the FSM model is the initial age of rifting phase due to a lack of well penetration that fully covered the Syn-rift sequence and the limited biostratigraphic data. Therefore, two different age scenarios are examined in this study analogue from the age model as it was published in the Malay Basin locating to the south of study area. Once the FSM model was built, the last step was to calibrate the prediction result with the actual well result and the conventional seismic data to achieve the best accuracy and to increase the confidence on using the model. The FSM model was successfully reproduced the stratigraphic successions of the Syn-rift sequence in West Arthit area. The base case model was chosen from the age scenario of 27.0-23.1 Ma which exhibited four major cyclicities and matched with seismic mapping. The study area had two depocenters, one in the northwest and another one in the southeast. The northern sub-basin was deepened earlier during the first rifting phase whereas the southern sub-basin was subsided later after the second rifting period. With the increase in sedimentation rate and subsidence rate during the third rifting phase, both depocenters were shallowed up and then become a shallow lake covering the whole study area. The last lifting phase coincided with the thermal subsidence that occurred and affected across the region; therefore, the regional extensive lacustrine accumulated in the study area. The results from this study provided a crucial information on petroleum system especially depositional architecture, reservoir distribution, and potential source rock identification, which were incorporated into the planning of future exploration targeting in this field. This study demonstrates the new innovative approach to determine the basin evolution and to understand the variation on depositional setting in the study area with limited well data. This approach also creates the project value by supporting the planning of future exploration and development wells. Furthermore, this technique can be applied to all projects to increase the discovery rate and to add the field reserves.

With the determination towards sustainable growth, PTTEP has a commitment to achieve Net Zero Greenhouse Gas Emissions by 2050. Therefore, the Carbon Capture Utilization and Storage (CCUS) project in the Gulf of Thailand was initiated to evaluate the CO2 storage capacity in Bongkot and Arthit fields. Three categories of storage potential were considered including shallow aquifers and depleted gas reservoirs together with storage potential in oil rim reservoirs by using CO2 enhanced oil recovery (CO2-EOR) method. The storage potential in shallow aquifer was targeted on porous rock located between seabed and top producing reservoirs which were identified in seismic and/or well data and reached by existing platforms. For the inventory of depleted gas reservoirs, the cumulative gas production volume was allocated to an individual reservoir, which signified storage size and injectivity of reservoir. The depleted gas reservoirs were focused on ones where a great amount of gas has been produced. For the CO2-EOR candidates, all oil rim reservoirs were reviewed and included in the study. The calculation of oil gain, CO2 injection requirement, and CO2 storage potential were based on the statistical data of Water-Alternating-CO2 fields. The inventory of CO2 storage potential from three categories were compiled with the information of 1) platform name, 2) remaining reserves, 3) distance from processing platforms, and 4) CO2 storage volume. After considering the CO2 storage potential, two platforms were considered as the most suitable for two fields equipped with CO2 removal units. In addition, the CCS development study considered an option to improve CO2 removal performance of the membrane in order to recover more hydrocarbon from flared gas. After the preliminary technical evaluation, the detailed study with reservoir simulation will be conducted in order to ensure the injectivity at reservoir level, the optimization of injection well number, and the integrity of containment. The injection plan will be formulated, and the investment cost estimation of CCS project can be refined accordingly. This CCUS study was initiated to reduce the CO2 emission from production fields under PTTEP. Currently, there are more than 20 CCUS projects around the world with only a few projects at the stage of CO2 injection. It requires good collaboration among subsurface and surface teams to increase confidence in storage suitability assessment. This project provides an example of multi-disciplinary integration and robust workflow starting from CO2 storage identification, volume calculation, to candidate ranking for further detail study.

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The objective of this study is to characterize sand reservoirs by using seismic inversion technique, the results were used to support CO2 storage potential identification and reservoir modeling works (storage volume calculation). The key storage targets are the saline aquifers and depleted reservoirs. These main targets were interpreted as a deposition of distributary channels occurring in the Paleo Chao Praya delta plain during Miocene. The results of this project contribute to a more accurate volume calculation for CO2 storage capacity. A rock physics feasibility analysis was carried out to understand a link between the observed seismic responses and the rock properties. Based on conclusions made in the rock physics analysis, P-Impedance could be used to delineate sand reservoir from shale, thus, a post-stack deterministic seismic inversion was selected for this reservoir characterization. Bayesian litho-classification method justifies lithology types by Probability Density Function (PDF) of P-Impedance, the resulting PDF was then applied to the inverted relative P-Impedance to create sand probability and lithology (most probable) volumes. Then, posterior validation of the lithology classification results was performed by investigating the match between the actual upscaled lithology log and pseudo lithology log from the Bayesian classification. Furthermore, the sand probability maps of the target reservoirs show an acceptable sand distribution response to the distributary channels in lower coastal plain environment that is consistent with the well results. The results of this work demonstrate how quantitative interpretation (QI) can successfully improve confidence in sand reservoirs mapping, in an area of complex faulted reservoir interval. The results presented here are beneficial for storage potential identification and reservoir modeling part, which can provide a more precise estimation of CO2 storage volume. The final results of the QI study provide good quality seismic inversion products and lithology cube, which enabled sand delineation at the target CO2 storage level. The key contributors have been ensuring optimal seismic input data, being in this case achieved through using a PSDM seismic processing technology, careful parameterization of seismic inversion process, and utilization of Bayesian classification method for lithology classification.

To prolong the field life of The Suphanburi oil field, an additional enhanced oil recovery (EOR) process is required. Dynamic reservoir modeling will need to be performed to maximize the EOR strategy. However, achieving the right result is a challenge as the field has a complex depositional environment and high heterogeneity, resulting in a high uncertainty of the dynamic reservoir model. A new reservoir model is proposed and created. The new model has been purposely built to capture the heterogeneity of the field by incorporating the newly interpreted geological concept of the field, together with quantitative seismic interpretation results. First, the new geological concept is interpreted from well data into "depofacies". The depofacies describe both depositional environment and lithofacies. Next, quantitative seismic interpretation is performed to capture the spatial variation of the reservoir and the predefined facies. Lastly, the reservoir model is built by first generating the depofacies. The reservoir or sandstone is then modeled specifically into each pre-modeled depofacies. As a result, the new reservoir model can better capture reservoir heterogeneity, resulting in a better EOR strategy.