The growing demand for hydrocarbons has driven the exploration of riskier prospects in depths, pressures, and temperatures. Substantial volumes of hydrocarbons lie within deep formations, classified as high pressure, high temperature (HPHT) zone. This study aims to delineate hydrocarbon potential in the HPHT zone of the Malay Basin through the integrated application of rock physics analysis, pre-stack seismic inversion, and artificial neural network (ANN). The zones of interest lie within Sepat Field, located offshore Peninsular Malaysia, focusing on the HPHT area in Group H. The rock physics technique involves the cross-plotting of rock properties, which helps to differentiate the lithology of sand and shale and discriminates the fluid into water and hydrocarbon. The P-impedance, S-impedance, Vp/Vs ratio, density, scaled inverse quality factor of P (SQp), and scaled inverse quality factor of S (SQs) volumes are generated from pre-stack seismic inversion of 3D seismic data. The obtained volumes demonstrate spatial variations of values within the zone of interest, indicating hydrocarbon accumulation. Furthermore, the ANN model is successfully trained, tested, and validated using 3D elastic properties as input, to predict porosity volume. Finally, the trained neural network is applied to the entire reservoir volume to attain a 3D porosity model. The results reveal that rock physics study, pre-stack seismic inversion, and ANN approach helps to recognize reservoir rock and fluids in the HPHT zone.

Traveltime is one of the propagating wave’s components. As the wave propagates further, the traveltime increases. It can be computed by solving wave equation of the ray path or the eikonal wave equation. Accurate method of computing traveltimes will give a significant impact on enhancing the output of seismic forward modeling and migration. In seismic forward modeling, computation of the wave’s traveltime locally by ray tracing method leads to low resolution of the resulting seismic image, especially when the subsurface is having a complex geology. However, computing the wave’s traveltime with a gridding scheme by finite difference methods able to overcomes the problem. This paper aims to discuss the ability of ray tracing and fast marching method of finite difference in obtaining a seismic image that have more similarity with its subsurface model. We illustrated the results of the traveltime computation by both methods in form of ray path projection and wavefront. We employed these methods in forward modeling and compared both resulting seismic images. Seismic migration is executed as a part of quality control (QC). We used a synthetic velocity model which based on a part of Malay Basin geology structure. Our findings shows that the seismic images produced by the application of fast marching finite difference method has better resolution than ray tracing method especially on deeper part of subsurface model.

Penyu Basin is a complex, intracratonic basin, situated on the northern Sunda Shelf. This basin formed during Oligocene, and geological setting of this area is a typical Southeast Asian Tertiary rift system. An oil discovery has been made in X-Block of Penyu Basin. However, it was relinquished in 2006 due to the non-commercial oil discovery. X-Block consists of mostly monoclinal structures that do not seem to provide an efficient trapping mechanism because of the very low reliefs. Three wells have been drilled in X-Block and tested primarily on the structural traps, mainly the basement drape structures. This research aims to analyze the stratigraphic traps, focusing on channel features. This is done with the aid from seismic geomorphology. This method helps examine buried landforms by using seismic data as a tool. By seismic geomorphology study, several channel features can be recognized. Most of the channels can be found in upper and middle part of the seismic section. As going deeper to the bottom section, only lineaments of faults are visible. In the upper part of the seismic section, straight and long channel features can be observed and as moving downwards, the channel sinuosity increases resulting in meandering channel. From this seismic geomorphology study, it confirms that there are channel systems in X-Block of Penyu Basin.

The application of controlled-source electromagnetic (CSEM) in hydrocarbon exploration significantly facilitates the detection of economic hydrocarbon. The method captures anomalies through the resistivity contrast between the overburdens and hydrocarbon-bearing lithologies. In most cases, the resistivity contrast is only prominent when there is sufficient hydrocarbon saturation. K-Field is situated on the continental shelf of Sarawak Basin, a sub-mature area for oil and gas in the Central Luconia province. Despite the low saturation of the gas in the Cycle VI sand, the seismic data shows a strong amplitude in the shallow section. Therefore, this study is conducted to assess the change of resistivity and CSEM response to the gas saturation and thickness variations of thin-gas sand in the K-Field. 3D resistivity models and three exploration wells are provided and two main methods are implemented in this study comprising the resistivity and CSEM forward modelings. The resistivity modeling is conducted using the Indonesia water saturation equation for different gas saturation scenarios and subsequently, the modeled resistivity is inputted in 1D and 2.5D CSEM forward modeling. The modeled CSEM response analysis is done by normalizing the modeled CSEM amplitude to the background or also known as normalized amplitude response (NAR). In gas saturation variation, the modeled resistivity showed an insignificant resistivity increase from 0.45Ωm to 0.55Ωm from wet case to 5% of gas and strongly increases to 35Ωm at 90% of gas saturation. The 1D CSEM NAR shows a very weak response of less than a 3% increase for 5% of gas and up to 230% increase for 90% of gas. In gas thickness variation, the CSEM NAR is weak and less than a 15% cutoff for all the tested thicknesses for 5% and 45% of in-situ gas. At 70% of gas, 25m is the minimum detected gas thickness with a 17.5% response increase, and at 90% of gas, the response is already strong at a minimum 5m thickness with a 35% increase. The modeled 2D CSEM responses also show that only 70% and 90% of gas sand layers in the K-field were delineated distinctively by the inline receivers with a 40% and 200% response increase respectively.