Forward stratigraphic modelling (FSM) is an evolving technology for understanding the geology between wells for the purpose of exploration and field development. As opposed to the use of geostatistics, this process-based modeling approach uses physical equations for key controls on deposition, such as initial bathymetry, eustatic sea level change, subsidence rate, wave energy, and other environmental conditions. The output simulation is a 3D cellular model with properties like lithology, porosity, and water depth. FSM results do not honor the interpretation from the drilled wells. This means that the prediction accuracy from the generated model may not be high. The current process of calibrating forward stratigraphic models is time-consuming and tedious. In this paper, we propose an automated workflow to improve the accuracy of a forward stratigraphic model by automating its calibration to facies data from wells. For our case study, we use an initial stratigraphic model of the Hanifa and Arab-D in central Saudi Arabia. The modeling area covers 430 km by 370 km, the cell size is 10 km, and the simulation time step is 100,000 years. In the resulting model, cells are assigned to seven index facies based on their lithology, wave energy, and water depth. Initially, we conducted a sensitivity analysis to identify the environmental parameters with critical influence on the final model. Subsequently, we ran several simulations with varying values for these critical parameters. To measure the match between the different simulation models and the observed well facies, we used facies from 16 wells. The simulation run with the highest match was used as the best forward stratigraphic model. Uncertainty maps, based on superimposing several simulations, were generated to check which areas of the simulation are replicated more often than others, meaning they bear, relatively, the lowest uncertainty. This approach may be used to evaluate the risk of drilling new wells in specific locations, or to provide a measure of the uncertainty for subsequent reservoir simulations. In the future, we will use seismic facies and attributes, in addition to well data, for the model calibration.

The Wasia Formation presents opportunities to explore for stratigraphic traps in the Saudi Arabian Rub’ Al-Khali Basin because it contains numerous interbedded reservoirs, sources, and sealing rocks. The mid-Cretaceous Wasia Formation includes a rudist carbonate platform with five, third-order sequences comprising, from oldest to youngest, Safaniya, Mauddud, Ahmadi, Rumaila, and Mishrif members. These members include proximal shallow-marine, highstand carbonate shoals at the platform margin in close proximity to fine-grained carbonate deposits in the Shilaf Basin. The resulting depositional cycles and stratigraphic architecture position muddy-tight seals, adjacent to porous shallow-marine carbonate-shoal bodies. Two members (Safaniya and lowermost Mishrif) have high organic-matter content situated in the oil window. Core data, well logs, seismic signals, and modern analogs were analyzed to help understand the Wasia deposition. Detailed correlations were made of well logs and neural network training was used to generate electro-facies. Next, supervised waveform analysis was used, correlated to five well log facies, to create five waveform facies including (1) lagoon, (2) back-shoal, (3) shoal, (4) slope, and, (5) basin facies. Sources of potential uncertainties include data processing, seismic to well ties, position of stratigraphic tops and seismic horizon interpretation. To minimize these, care was taken in data processing and a blind test was performed to validate the final interpretation. On the basis of integrating the aforementioned data with our waveform facies, a reference geological model was built demonstrating that potential stratigraphic traps are porous, shallow-marine carbonate shoals intercalated with muddy-tight slope deposits resulting in isolated, porous carbonate reservoir bodies sealed by tight rocks. For example, the Ahmadi Memberseal was deemed to be too thin to seal the oil in the underlying Mauddud. In addition, muddy-tight lowermost Mishrif Member strata are also too thin to seal oil in the underlying Rumaila. In the worst case, laterally extensive upper Mishrif reservoirs are not sealed by interbedded lateral seals even though the Aruma shale seals their tops. The two best trap configurations include (1) the first highstand lower sequence of the Mishrif reservoir sealed by interbedded extensive transgressive muddy Mishrif carbonates and (2) thick Ahmadi and lowermost Mishrif fine-grained carbonates sealing Mauddud and Rumaila highstand system tracts.

Objectives/Scope The challenge of optimizing exploration activities using numerical modeling is in having accurate model predictions with acceptable uncertainty tolerance for use in the decision making process. Unfortunately, the prediction results of typical numerical models bear major uncertainties, making any decision-making based on these models risky and greatly increasing exploration costs. The objective of this abstract is to develop an integrated workflow accounting for all geological processes from source to trap. Methods, Procedures, Process Aside from tectonic and structural influences, two major processes are responsible for hydrocarbon accumulation: deposition of sediments and fluid migration. Combining these two processes in a single modeling workflow is a powerful approach for accurate quantitative modeling of petroleum systems (forward modeling). This workflow can be used to predict new resources with some range of uncertainties. These uncertainties can be minimized by calibrating the integrated workflow with the well and seismic data (inverse modeling). Therefore, an optimization loop is added to the workflow to automatically calibrate the forward model to the available data: core description, horizons, borehole temperature, pressure, vitrinite reflectance, fluid analysis, known reservoir volume. Results, Observations, Conclusions This workflow was implemented for eastern Saudi Arabia (upper Jurassic). First of all, a forward depositional model was built to model the 3D facies distribution within the area of interest. To reduce the uncertainties associated with the 3D facies distribution, the forward depositional model was calibrated with well facies (facies from core description and predicted electro-facies using wireline logs). The 3D facies distribution derived from the calibrated depositional model was used to model and simulate the petroleum systems for the same area of interest (forward model). Data from the wells are used to calibrate the petroleum system model to improve its prediction capabilities. The combined model was used to quantitatively assess the four elements of the petroleum system: source rock, closure, seal, and reservoir rock. Reasonable agreement was obtained between the workflow prediction and the observed hydrocarbon accumulation in eastern Saudi Arabia. This agreement has provided enough confidence to the asset team to use the combined calibrated model in generating new prospects in the area of interest. Novel/Additive Information Source rock, closure, seal, and reservoir rock are key elements of identifying a petroleum system. Conventionally, each of these elements is evaluated independently from the others. Our quantitative workflow for prospect generation, using numerical modeling and simulation, is assessing prospect integrity by considering the four elements simultaneously. Moreover, to reduce the uncertainties associated with the prospect generation and prediction, this workflow is calibrated with the historical data (well, seismic, etc.) collected from the nearby area.