Initially, electrofacies were introduced to define a set of recorded well log responses in order to characterize and distinguish a bed from the other rock units, as an advancement to the conventional application of well logs. Well logs are continuous records of several physical properties of drilled rocks that can be related to different lithologies by experienced log analysts. This work is time consuming and likely to be imperfect because human analysis is subjective. Thus, any automated classification approach with high promptness and accuracy is very welcome by log analysts. One of the crucial requirements in petroleum engineering is to interpret a bed’s lithology, which can be done by grouping a formation into electrofacies. In the past, geophysical modelling, petro-physical analysis, artificial intelligence and several statistical method approaches have been implemented to interpret lithology. In this research, important well log features are selected by using the Extra Tree Classifier (ETC), and then five individual electrofacies are constructed by using the selected well log features. Finally, a rough set theory (RST)-based whitebox classification approach is proposed to classify the electrofacies by generating decision rules. These rules are later on used to determine the lithology classes and we found that RST is beneficial for performing data mining tasks such as data classification and rule extraction from uncertain and vague well log datasets. A comparison study is also provided, where we use support vector machine (SVM), deep learning based on feedforward multilayer perceptron (MLP) and random forest classifier (RFC) to compare the electrofacies classification accuracy.

Accurate determination of water saturation is fundamental for predicting the amount of hydrocarbon fluid in reservoir, apart from monitoring fluid saturation changes whithin the reservoir using 4D seismic technology in the most efficient manner. In fact, although several techniques can be employed to predict water saturation, the well log based approaches have emerged as the most widely applied methods. Nevertheless, even though these techniques offer rather exceptional vertical resolution, it is a fact that lateral estimation is still an issue. As such, this study proposes a methodology that estimates the water saturation based on pre-stack seismic information by using artificial intelligence (AI) technologies, such as artificial neural network (ANN), adaptive neuro-fuzzy inference systems optimized by genetic algorithm (ANFIS-GA), as well as support vector machine (SVM). Therefore, the seismic data would go through a process known as seismic inversion and then transformed into seismic attributes, represented as SQp and SQs, this transformation is adherent to attenuation concept, which is associated to the rock physics. On top of that, data from two wells in Malaysian hydrocarbon field were employed to build the estimation models of ANN, ANFIS-GA, and SVM and then third well data was used as blind well to test unseen data. Furthermore, both SQp and SQs had been employed in the models as input variables, whereas the water saturation functioned as output. Other than that, several performance indices of statistical had been incorporated in this study so as to perform a comparison between the performances exerted by the selected models. As a result, the findings indicated thatalthough all models showed an acceptable agrrement with the experimental data to some extent, the ANFIS-GA based model had a better performance over other models. The present study show that the AI technology can be incorporated in water saturation prediction workflow from pre-statck seismic data which result in superior accuracy and performance in fluid flow calculations and time lapse seismic analysis.

Separation of lithology from pore fill remains a daunting task, even if using elastic inversion. This is one of the unsolved problems for hydrocarbon prediction in Malaysian basins because of complexities coupled with lithologic trends and fluid responses, which produce an ambiguity due to equivalences. We address this problem and propose a unique transformation in which decoupling can be achieved. From the output of a constrained elastic inversion, a relationship of V_P/V_S and new attributes SQp and SQs related to attenuation by linking through rock physics can be derived. They are good proxies for gamma rays (SQp) and resistivity logs (SQs) and can clearly identify the zone of interest with hydrocarbon potential. These attributes can also be used for petrophysical-properties prediction with high accuracy. This breakthrough would have a considerable effect on hydrocarbon prediction and lithology discrimination. Other than being a hydrocarbon and lithology indicator, SQp and SQs can also potentially be used as input for prediction of petrophysical properties such as porosity, net-to-gross, water saturation, and resistivity transform from elastic properties.

The aim of this study is to investigate the estimation of density from the Hounsfield unit of cone beam computed tomography data in dental imaging, especially for dental implant application. A jaw phantom with various known densities of anatomical parts (e.g. soft tissue, cortical bone, trabecular bone, tooth enamel, tooth dentin, sinus cavity, spinal cord and spinal disc) has been used to test the accuracy of the Hounsfield unit of cone beam computed tomography in estimating the mechanical density (true density). The Hounsfield unit of cone beam computed tomography data was evaluated via the MIMICS software using both two-dimensional and three-dimensional methods, and the results showed correlation with the true density of the object. In addition, the results revealed that the Hounsfield unit of cone beam computed tomography and bone density had a logarithmic relation, rather than a linear one. To this end, the correlation coefficient of logarithmic correlation (R² = 0.95) is higher than the linear one (R² = 0.77).

Understanding the behavior of elastic properties to get the best discrimination on lithology and fluid prediction is the most important knowledge to minimize the pitfalls in the interpretation. Rock Physics Template (RPT) which is geologically constrained rock physics model can serve the information about the lithology and fluid separation; hence it can guide the interpreter to reduce the pitfalls in prediction and misinterpretation. The objective of this paper is to show the performance of new RPT in discriminating the lithology and pore fluids. The new RPT has been developed based on SQp and SQs attributes. These attribute were derived from attenuation–rock physics approximation. The new RPT was tested on soft sediment and cemented model and it was also applied on the real dataset from a field in Malay Basin. The performance of new RPT on lithology and pore fluid separation was compared with the existing RPT generally used in the industry. The results show that the new RPT which is based on SQp and SQs attributes give better separation in lithology and hydrocarbon prediction. The lithology effect was separated in the SQp domain and pore fluid was separated in the SQs domain which are both domain are orthogonal to each other. The implementation of this RPT on the real data shows that the gas sand separated clearly from wet sand and coal layer. The pitfalls due to the coal masking can be solved by implementing these attributes. The lithology and pore fluid were separated optimally. This new RPT provides an optimum separation on lithology and pore fluid effect. Pitfalls due to Coal mask can be separated properly; hence this new technology potentially can be used to reduce the pitfalls and uncertainty in hydrocarbon prediction. Further improvement is still required on the extended applications of this new technology on other cases such as low resistivity low contrast pay prediction.

This study identified the Pleistocene depositional succession of the group (A) (marine, estuarine, and fluvial depositional systems) of the Melor and Inas fields in the central Malay Basin from the seafloor to approximately −507 ms (522 m). During the last few years, hydrocarbon exploration in Malay Basin has moved to focus on stratigraphic traps, specifically those that existed with channel sands. These traps motivate carrying out this research to image and locate these kinds of traps. It can be difficult to determine if closely spaced-out channels and channel belts exist within several seismic sequences in map-view with proper seismic sequence geomorphic elements and stratigraphic surfaces seismic cross lines, or probably reinforce the auto-cyclic aggregational stacking of the avulsing rivers precisely. This analysis overcomes this challenge by combining well-log with three-dimensional (3D) seismic data to resolve the deposition stratigraphic discontinuities’ considerable resolution. Three-dimensional (3D) seismic volume and high-resolution two-dimensional (2D) seismic sections with several wells were utilized. A high-resolution seismic sequence stratigraphy framework of three main seismic sequences (3^rd order), four Parasequences sets (4^th order), and seven Parasequences (5^th order) have been established. The time slice images at consecutive two-way times display single meandering channels ranging in width from 170 to 900 m. Moreover, other geomorphological elements have been perfectly imaged, elements such as interfluves, incised valleys, chute cutoff, point bars, and extinction surfaces, providing proof of rapid growth and transformation of deposits. The high-resolution 2D sections with Cosine of Phase seismic attributes have facilitated identifying the reflection terminations against the stratigraphic amplitude. Several continuous and discontinuous channels, fluvial point bars, and marine sediments through the sequence stratigraphic framework have been addressed. The whole series reveals that almost all fluvial systems lay in the valleys at each depositional sequence’s bottom bars. The degradational stacking patterns are characterized by the fluvial channels with no evidence of fluvial aggradation. Moreover, the aggradation stage is restricted to marine sedimentation incursions. The 3D description of these deposits permits distinguishing seismic facies of the abandoned mud channel and the sand point bar deposits. The continuous meandering channel, which is filled by muddy deposits, may function as horizontal muddy barriers or baffles that might isolate the reservoir body into separate storage containers. The 3^rd, 4^th, and 5^th orders of the seismic sequences were established for the studied succession. The essential geomorphological elements have been imaged utilizing several seismic attributes.