Published on Carbon Management Technology Conference Orlando, Florida 2012

M4 carbonate field, a depleted gas field located offshore Sarawak, has been identified as a potential candidate for CO2 sequestration site in conjunction with another high CO2 field development and commercialization efforts. The field has undergone a feasibility study to evaluate potential geomechanical issues associated with CO2 injection. A detailed 3D simulation analysis was conducted to quantify the effective storage capacity in the M4 field, identify the optimum CO2 injection scheme and evaluate the trapping mechanism in the M4 field. A reservoir geomechanical study was also performed for the M4 field to evaluate the associated geomechanical issues pre, during, and post CO2 injection to assure a safe and long-term CO2 sequestration in the field.

First, the available field history matched black oil simulation model was successfully converted to a compositional 3D model, in which CO2 is treated and can be tracked as a separate component in the reservoir throughout the production and injection processes. A detailed study has then been conducted to understand the containment and analyze the effective CO2 trapping mechanisms. Different types of trapping mechanisms including hydrodynamic trapping, residual or capillary trapping, solubility trapping, and mineral trapping have been studied in detail. Hysteresis effect on CO2 sequestration and different trapping mechanism during and post CO2 injection has been also studied. In addition, various CO2 injection schemes have been also conducted to optimize the injection rate, sustainability, capacity, location, number of the wells, and favorable trapping mechanism for long-term sequestration. The study covered 20 years of gas production history and forecast followed by 10 years of CO2 injection in the selected optimum scheme and then monitoring part more than for 100 years after injection to assure the safe sequestration and potential CO2 leakage. Constraining to the initial reservoir pressure to assure cap rock integrity and potential leakages, the study showed that the field has the potential to store and sequestrate CO2 up to 40% bigger standard volume than gas initially in place (GIIP).

Published on Journal of Asian Earth Sciences 202 (2020) 104541

Conventional geological modeling methods are not capable to provide a precise and comprehensive model of the subsurface structures when dealing with insufficient data. Knowledge-based methods employing rule bases techniques are found vast applications in geoscience studies. These methods are applicable for petroleum reservoir geological modeling and characterizations, specifically for geologically complex structures. In this study, we present a knowledge-based seismic acoustic impedance inversion method that employs a rule-based method for porosity estimation. The backpropagation algorithm and the fuzzy neural network are also used in the methodology for parameter optimization and the definition of a nonlinear relationship between seismic attributes and porosity of the reservoir rock. The methodology initiates by seismic acoustic impedance inversion, followed by conventional porosity estimation. Subsequently, a knowledge base was designed by investigation on more than 24 published case studies. This knowledge base was used for the definition of rules and optimization number of rules and to improve the efficiency of the inference engine. The porosity model obtained by the conventional method in the previous step would be used for primary evaluation of the rules. The extracted rules and optimized number rules then would be used for rule-based porosity estimation. The methodology was applied on a petroleum field containing two heterogeneous reservoir formations. The result of the application of the proposed approach was evaluated with core analysis, thin sections, and drilling data. Consistency of results obtained by the proposed method with geological data has shown its capability to resolve the problem of insufficient data in reservoir geological modeling.

Published on EAGE Asia Pacific Virtual Geoscience Week 2021

Machine learning is needed to predict the contact angle in the shale using the process parameters and TOC and the Mineralogy of the shale. Mineralogy and Total Organic Carbon (TOC) content are some of the important parameters to be evaluated for reservoir characterization. Wettability is the capability of a liquid to remain in contact with a solid surface affected by the balance of both intermolecular forces of adhesive force (liquid to the surface) and cohesive force (liquid-liquid). The study aims to investigate the effect of both parameters, TOC, and mineralogy on shale wettability with a case study of Malaysian shale samples. The values for each parameter, TOC, and mineralogy are obtained through thermal pyrolysis and X-ray diffraction, respectively. Advance application is carried out by applying the machine learning technique to predict the effect of shale TOC and mineralogy on the wettability of the reservoir rock. The application aims to develop a machine learning program using the algorithm of Support Vector Machine or Gaussian Process Regression to successfully predict the contact angle. The developed model has succeeded in predicting the contact angle for different input variables of the machine learning model with high r squared values.

Published on SPE Symposium: Production Enhancement and Cost Optimization, 2017

Behind casing opportunity (BCO) is an important production enhancement solution that extends field life in brownfield development, specifically at low oil prices and the current cost control environment. BCO potentially offers, low-hanging fruit in terms of cost and realizing cheap oil, but successful maturation and realization of these opportunities rely on a clear understanding of reservoir behavior and uncertainties. Historically, our ability to meet planned objectives has been challenging and in fact, only 50% of these opportunities have met their objectives, the remainder compromised by either integrity or reservoir performance issues. An unconventional approach has been tested by the subsurface team and is presented here.

The approach presented here focuses on the impact of workflow optimization, multidisciplinary and integrated work on instantaneous production gain. The sequential workflow includes data gathering, proper data preparation, and integrating basic data and information (brilliant at the basics) to reduce inherent risk and uncertainty. A significant improvement in conventional petrophysical modeling, using proper analogs, unlocks the potential resources which have been masked by low resistive elements. Detailed well modeling and performance analysis, highlights the potential gain from these low resistive pay zones. Coupled with a detailed screening process the opportunities with the highest probability of success (POS) and most favorable commercial feasibility are selected. The result demonstrates the huge upside potential for nominated low resistive pay zones that can be developed through BCO or horizontal infill targets. On the commercial side, it was recommended to execute the plan in a campaign for cost optimization purposes. It is expected to reduce operational costs by 30 percent and improve recovery through the realization of more drainage points. Progressive burning propellant stimulation has been initiated to maximize production gain by maximizing exposure to the reservoir and boosting flow capacity by a factor of two times through 25 feet of low perm section.