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A crucial role that significantly affects carbonate field development for hydrocarbon and carbon sequestration (CCS) projects is reducing uncertainty in rock type prediction. The carbonate reservoirs in Central Luconia Province, Malaysia, are significant economic worldwide reservoirs and are currently considered excellent candidates for Carbon Storage containers. The nature of these carbonate rock properties is visible and notably distinguishable at the core scale. To characterize significant petrophysical and geological factors of the distribution of the rock properties in the E11 carbonate build-up, this work proposes a sequence of processes (workflow) for obtaining spatial information about the organization using Kohonen Self-organizing maps. This work highlights the significant geological and petrophysical constraints on the distribution of rock properties in the E11 field. Using self-organizing maps, the predicted rock types were propagated among wells with no core available. Using this workflow, multiscale data is categorized according to "patterns". The phases include Phase 1: Detailed core description, Phase 2: Microscope sections description, Phase 3: Well logs analysis, Phase 4: Well logs analysis, and Phase 4: Self-organizing maps using IPSOM module in Techlog software. Considering the stratigraphic organization, juxtaposition, and proportions, the anticipated rock type closely resembles the rock types identified by core description manually. The results allow a comprehensive understanding of flow behavior in carbonate tight and reservoir rock types.

Reducing uncertainty in 3D carbonate rock type distribution is a critical factor that profoundly impacts field development for hydrocarbon or carbon capture and storage (CCS) projects. Miocene carbonate reservoirs in the Central Luconia offshore region are economically important global gas reservoirs. The nature of these carbonate rocks can be visually distinct in the core and the multiscale reservoir heterogeneity might vary in scale from the 100-m scale to the sub-millimeter scale. This work presents a series of steps workflow to obtain spatial information about the organization scheme of carbonate rock types, and capture the most important petrophysical and sedimentary controls on rock property distribution in the E11 field, a carbonate buildup, located in Central Luconia Province, offshore Malaysia. The spatial data were generated from a supervised neural Kohonen algorithm. The rock types predicted with this workflow were propagated using IPSOM probabilized self-organizing maps SOM. This tool is used for classifying multivariate data samples according to “patterns” or multivariate responses. The workflow includes several steps: A Step 1—Core data description, B Step 2—Thin section description, C Step 3—Well log interpretation, and D Step 4—IPSOM probabilized self-organizing maps for facies prediction SOM. The depth plots of the predicted rock type showed close correspondence to the core-based rock types in terms of the stratigraphic organization of tight and reservoir layers, proportions, and juxtaposition. This result is sufficient to merit the application of the rock type logs into a future porosity model of the E11 field, and to understand the lateral and vertical distribution of tight and reservoir rock types of distribution. The results can be used to build a future realistic digital twin of the subsurface, and in digital geological modeling.

Stratigraphic forward modeling (SFM) is an innovative approach to subsurface facies prediction at the basin scale that augments and overcomes some of the limitations of conventional seismic, well, and analog data. As a multidisciplinary approach to play characterization, SFM improves the efficiency of current workflows, which is important given the current downward pressure on capex in oil and gas companies. A 2D SFM study on data from Browse basin, NW Australia, was conducted to enhance the prediction of facies distribution and improve play characterization by integrating SFM with other disciplines. The work started with seismic interpretation and depth conversion. Then, a third to fourth-order sequence stratigraphy interpretation was performed to determine the main sequence boundaries, maximum flooding surfaces, and a relative sea-level curve. The sequence stratigraphy results were later used to infer some of the inputs and parameters of the SFM model. The model simulates the deposition of clastic and carbonates from the Turonian (Late Cretaceous) to the present day. The results from the model were used to validate some of the geological concepts and the seismic interpretation. In addition, the approach enabled the prediction of reservoir quality, reservoir distribution, the presence of the seal, and the quantification of erosion. A 2D petroleum system model (PSM) covering the area from the Yampi shelf to the Seringapatam sub-basin was built using seismic interpretation, regional tectonic information, source rock geochemistry, and paleo heat flow. The results from SFM were integrated into a 2D PSM by resampling facies and erosion properties for each of the finely subdivided layers. The high-resolution 2D PSM with refined facies was simulated in geological time to model the basin evolution and its impact on all elements and processes of the petroleum system of Browse basin, which have been validated with nearby fields. As a result of this integrated approach, the risk of charge and entrapment in prospective stratigraphic traps was better understood and quantified. In addition, this approach helped to increase yet-to-find (YTF) hydrocarbon resources by accurately predicting reservoir distribution and extent. The generation of a 2D SFM and its integration within a multidisciplinary approach to predict facies represents a novel addition to exploration workflows. Adopting such an approach can improve significantly on the understanding of hydrocarbon entrapment and further reduce exploration risks.

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