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.
A subsurface resistivity model is important in hydrocarbon exploration primarily in the controlled-source electromagnetic (CSEM) method. CSEM forward modelling workflow uses resistivity model as the main input in feasibility studies and inversion process. The task of building a shaly sand resistivity model becomes more complex than clean sand due to the presence of a shale matrix. In this paper, a new approach is introduced to model a robust resistivity property of shaly sand reservoirs. A volume of seismic data and three wells located in the K-field of offshore Sarawak is provided for this study. Two new seismic attributes derived from seismic attenuation property called SQp and SQs are used as main inputs to predict the volume of shale, effective porosity, and water saturation before resistivity estimation. SQp attribute has a similar response to gamma-ray indicating the lithological variation and SQs attribute is identical to resistivity as an indicator to reservoir fluids. The petrophysical predictions are performed by solving the mathematical step-wise regression between the seismic multi-attributes and predicted petrophysical properties at the well locations. Subsequently, resistivity values are estimated using the Poupon-Leveaux (Indonesia) equation, an improvised model from Archie’s to derive the mathematical relationship of shaly sand’s resistivity to the volume and resistivity of clay matrix in shaly sand reservoirs. The resistivity modeled from the predicted petrophysical properties distributed consistently with sand distribution delineated from SQp attribute mainly in southeast, northeast, and west regions. The gas distribution of the net sand modeled by 5% and 90% of gas saturation scenarios also changed correspondingly to SQs attribute anomaly indicating the consistent fluid distribution between the modeled resistivity and SQs attribute.
One of false positives in seismic amplitude associates with the presence of low saturation gas. The amplitude response is ambiguous prominently in shallow subsurface due to similar amplitude of fizz gas to highly saturated gas reservoir. This pitfall is demonstrated in Gassmann’s fluid substitution modelling which drastic velocity reduction is modeled by 5% of gas. The strong reduction induces anomalous bright spot in seismic similarly manifested by a highly gas reservoir. In pore fluid characterization, seismic amplitude of a brine reservoir is well distinguished from gas by change of response polarity from negative to positive amplitude due to higher brine modulus than gas. To a lesser extent, the negative amplitude of gas sand almost identical between commercial and non-commercial gas trap and difficult to distinguish in seismic. The similar negative amplitude of the gas cases caused by a very low gas modulus resulting to sudden drop of rock bulk modulus despite with the presence of low gas percentage. Thus, seismic velocity slows down when travels through a gas sand medium. Integration of controlled-source electromagnetic (CSEM) method in hydrocarbon prediction reduces exploration risk by only detecting commercial hydrocarbon. It exploits resistivity contrast between overburden and hydrocarbon reservoir which highly controlled by hydrocarbon saturation. CSEM normalized response exceeds the cut off response by a minimum saturation of 70% of gas. Less than 70% of gas, CSEM response is below the cut off response which considered as insignificant anomaly in CSEM measurement. It becomes a key strength as this method reduces amplitude uncertainty in seismic due to low saturation gas and potentially improves the chances of economic discovery in hydrocarbon exploration.
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.
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