EAGE/FESM Conference on Petrophysics meets Geoscience: Unlocking Reservoir Potential in a Dynamic Energy Landscape

Carbonate reservoirs in the Central Luconia field, Sarawak, Malaysia, present considerable challenges due to their heterogeneity and complex pore systems, which complicate reservoir characterization and hydrocarbon recovery. Traditional log-based methods often fail to accurately represent lithofacies variation and pore structure, resulting in discrepancies in dynamic reservoir models. To address these limitations, this research introduces a machine learning (ML)-based approach to enhance rock type classification and permeability estimation.

The proposed methodology follows a three-step workflow. First, rock type classification is performed using lithofacies, depositional facies, and Hydraulic Flow Unit (HFU) approaches. These are derived from capillary pressure data and analyzed using Rock Quality Index (RQI) and Flow Zone Indicator (FZI) methods. Next, well log data are classified into electrofacies using the Self-Organizing Map (SOM) algorithm, with refinement via core-calibrated supervised learning and Multi-Graph Based Clustering (MRGC) to optimize accuracy. Finally, permeability prediction is conducted using lithofacies-based and HFU-based models, integrating key reservoir parameters.

This methodology is applied to the field of carbonate reservoir. The study compares the effectiveness of the three classification schemes in predicting permeability, validated against core data. The results aim to provide insights into the capability of each method in capturing reservoir heterogeneity and improving subsurface model reliability.

This study encounters several challenges during petrophysical interpretation, including: 1) inconsistency in sonic data resulting from dipole or monopole measurement; 2) variations in data quality and inconsistencies between different contractors; 3) inconsistencies in data resolution across elastic logs (e.g., density vs. velocity); and 4) facies misclassification during petrophysical evaluation. To overcome these challenges, integrated seismic petrophysics and rock physics workflow is required to strengthen and constraint the petrophysical interpretation. This workflow involves log conditioning (LC), seismic petrophysics (SPP), and rock physics modeling (RPM), which requires multiple iterations. Seismic petrophysics addresses the limitations of conventional approaches, which typically limit calibration to the reservoir and my introduce inaccuracies in the surrounding non-reservoir sections. The result delivers a reliable and consistent rock physics interpretation with correctly represents the elastic responses. This study extends a previously published integrated workflow applied to the Birkhead Formation ( Cunha et. al., 2024 ), narrowing the focus to the integrated seismic petrophysics and rock physics modeling components.

Field S, located approximately 150 km offshore Terengganu, Malaysia, is poised for significant redevelopment with up to 30 infill wells planned over five years. To support this expansion and manage subsurface uncertainties, an ensemble-based reservoir modeling workflow was implemented. A comprehensive ensemble of 100 reservoir models was generated to represent key geological and engineering uncertainties, including petrophysics, facies distribution, fluid contacts, and PVT data. These models were dynamically conditioned to historical production data using an ensemble Kalman smoother, allowing iterative history matching and uncertainty reduction, particularly near existing producers. Post-history matching, representative models capturing P10, P50, and P90 cumulative production outcomes were selected to produce probabilistic forecasts under various infill drilling scenarios. This approach provided a robust framework for uncertainty quantification and risk-informed decision-making, supporting economic evaluation and development planning. The workflow is repeatable and scalable, enabling rapid updates as new data emerges. Results demonstrate the practical value of ensemble-based modeling in reducing uncertainty and optimizing field development strategies in complex offshore environments.

Reservoir development has become increasingly challenging due to geological uncertainties, necessitating more efficient well placement strategies to optimize recovery and production. Among the most cost-effective and widely available data sources is Gas While Drilling (GWD), which has often been undervalued due to perceptions of inaccuracy and limited fluid discrimination capability.

Recent advancements in drilling technology and data analysis have begun to challenge this perception. This paper presents a workflow and application of GWD for fluid identification and well placement in a geosteering well (Well AB-4) during a development drilling campaign offshore Sarawak. The integration of advanced gas analysis with Logging While Drilling (LWD) data has proven effective in reducing fluid-type uncertainty, enhancing real-time decision-making, and supporting accurate reservoir navigation. The near real-time and cost-effective nature of GWD contributes significantly to optimizing operations and achieving targeted production outcomes.

Accurate rock typing is critical for assigning reservoir properties and improving volumetric estimates and reservoir models. Traditional methods—based on petrophysical cutoffs or indices like RQI and FZI—often struggle in geologically complex settings such as fluvio-marine reservoirs, where petrophysical properties and lithofacies show weak correlation. This study addresses that challenge by integrating lithofacies and electrofacies data using a supervised machine learning approach.

Focusing on the Group XY Sands of the Alpha Field, a highly heterogeneous fluvio-marine reservoir, lithofacies descriptions from five wells were combined with well log data to train a Random Forest classification model. The model effectively harmonized geological and petrophysical inputs, producing consistent and geologically reasonable rock type predictions.

Results showed strong agreement with core descriptions in cored intervals and logical facies assignments in uncored zones. At Alpha-3, the model identified subtle rock quality variations missed by traditional methods. Across all wells, the model demonstrated robust generalization and improved resolution in medium to poor quality intervals.

This integrated machine learning workflow enhances rock typing accuracy in complex reservoirs and supports more confident reservoir characterization and development planning.

This study presents an integrated petrophysics, rock physics, and AVO modeling workflow applied to de-risk a carbonate prospect in the Cycle IV Carbonate play of Central Luconia. The target platform (Y) exhibited a Class III AVO anomaly—uncommon in nearby analogues—and posed significant calibration challenges due to poor log data quality in offset well X-1, caused by total mud loss and casing constraints. Through iterative petrophysical evaluation, elastic logs were reconstructed and used to simulate porosity-fluid scenarios. AVO modeling revealed that only high-porosity, gas-saturated models reproduced the observed seismic response. This interpretation was validated by a successful exploration well, confirming high porosity and gas saturation in the carbonate. The study demonstrates the effectiveness of an integrated petrophysical and geophysical workflows in mitigating data limitations and reducing exploration uncertainty in complex carbonate settings.

This paper highlights the challenges in identifying hydrocarbon presence and computing representative water saturation particularly in silty, low mobility, thin lamination and high clay bound water reservoirs. The complexity is further compounded with inadequate pressure, sample and core data to ascertain the fluid type, contact and validating the reservoir properties. Major intervention in the data acquisition plan has been revoked to acquire fit-for-purpose data for field development derisking including well test and coring. With respect to saturation computation, advanced core analysis covering mercury injection, centrifuge capillary pressure and NMR analyses guided by the lab best practices were performed to validate the range of saturation input used for volumetrics calculation. Discussion on the results taken from different reservoir cycles and hence representing varying reservoir quality will be entailed to test the methodology robustness and capture the uncertainty in the workflow. Results are also integrated with geological findings from XRD, thin section and petrography analyses to validate the observations from the proposed methodology which can be replicated for future projects.

Introduction

Reservoir surveillance in mature fields faces challenges such as complex wellbore geometries, aging completions, and gravel packs, which, coupled with high E-line costs, require strategies that maximize data value. Advanced cased-hole saturation logging using Pulsed Neutron Logging (PNL) offers a solution to identify untapped reserves and assess hydrocarbon potential behind casing.

Method and/or Theory

A case study from a 40-year-old field in the Balingian basin, Malaysia, applied Pulsar* technology to evaluate two high water-cut zones and screen additional opportunities. Pulsar integrates Sigma, C/O ratio, Hydrogen Index (HI), Inelastic Gamma Spectroscopy, and FNXS for robust saturation profiling, even in gravel-packed intervals. The team executed a hybrid high-speed pass (GHS mode) for comprehensive wellbore assessment within time and cost constraints, followed by targeted low-speed passes for high-resolution data in zones indicating hydrocarbons. Real-time interpretation enabled dynamic optimization, and PNX estimates aligned closely with reservoir models, validating predictive simulations.

Conclusions

The approach identified four new oil-bearing intervals—two scheduled for immediate production (0.2 MMSTB potential) and two for future optimization—extending well life by five years. This case demonstrates the value of integrating Pulsar technology, agile workflows, and real-time interpretation to transform routine logging into a strategic, reserve-adding intervention.

This study aims to evaluate the geological and diagenetic mechanisms controlling notably low permeability within clastic sedimentary reservoirs of the Early Miocene lower coastal plain in Lower Cycle II. The focus is on characterizing reservoir facies and tight reservoir distribution both vertically and horizontally and provide optimizing production strategies.

Key workflows include lithofacies identification and diagenetic feature analysis through petrographic analyses, XRD, and SEM, as well as electrofacies interpretation based on characteristic log response in non-core wells. Seismic inversion is utilized to define facies and property distributions, supporting the selection of technically viable production strategies.

The result indicate that the igneous intrusions are the primary factor of permeability reduction in lower coastal plain setting. These intrusions lead to thermochemical alteration and kaolinite enrichment. A comprehensive integrating geological context, petrophysical interpretation and seismic inversion reveals a widespread distribution of tight gas sand probability within specific interval in Lower Cycle II. The study enhances the accuracy of recovery volume estimation in low-permeability clastic systems and improve predictions of tight reservoir distribution in undrilled area. Additionally, advanced techniques such as hydraulic fracturing and horizontal drilling could potentially boost production rates by approximately 4 to 6 times over current estimates.

Several challenges are observed in a clastic formation due to the presence of extensive coal beds, which contributes to wellbore instability and significantly impact seismic amplitude responses. Additionally, the limited availability of reliable shear log data often presents a constraint, as the Vp/Vs ratio is essential for distinguishing lithofacies and evaluating rock mechanical properties. In this study, the widely used Keys and Xu rock physics model is applied to model elastic responses of the clastic mixed sediment. In general, the integrated seismic petrophysics-rock physics workflow includes log data conditioning, seismic petrophysical interpretation, and rock physics modeling. The final rock physics model (RPM) output predicts compressional velocity, shear velocity, and generates the Vp/Vs ratio, capturing lithological and fluid variations consistent with petrophysical volumes and geology. These results provide more meaningful inputs for subsequent seismic inversion and enhance interpretation in complex coal-bedded intervals.