
 To quantify the uncertainty in reservoir performance, it is common to build ensembles of models that sample the space of possible reservoirs that are consistent with the available data. To evaluate the spread of possible outcomes, simulations experiments are run for each model in the ensemble to calculate for instance recovery factor. The geoscreening workflow is a common way to do this systematically and in a reasonable time. It can work as follows: First, run simulations with simplified physics to calculate recovery factor for every model in the ensemble. Then, use recovery factor (and other quantities) to rank and select representative models for high, medium, and low performance scenarios that can be used for full field simulations.
 In this paper we present an application of the multiscale sequential fully implicit (MS SFI) framework to simulate extremely complex high-resolution models with simplified physics. This enables us to perform fast evaluations of geological uncertainty, such as in the geoscreening workflow. The multiscale SFI method computes each timestep in two steps: First, it solves a nonlinear equation for pressure (and flow). Then, it solves a nonlinear equation for saturations and mole fractions. The pressure equation is solved iteratively using a multiscale approach.
 The MS SFI method has recently been made generally available in a commercial reservoir simulator and can easily be benchmarked with a state-of-the-art fully implicit (FI) method. The MS SFI method was used to successfully simulate a realistic high-resolution geological model in a practical time frame, achieving approximately 10 times speedup in CPU time compared to the FI method. This demonstrates the ability of the MS SFI method to effectively deal with extremely complex models, enabling fast quantification of geological uncertainty with a shorter turnaround time. In many instances the MS SFI method enables simulation of large models at the original geological resolutions without the need for upscaling.
 Finally, we demonstrate how the MS SFI method benefits a geology screening workflow and discuss future use of the MS SFI framework to create fit-for-purpose simulation engines for other workflows.

Multiscale Reservoir Simulation of High-Resolution Models

Abstract The West Nile Delta basin is a significant petroleum region in the Mediterranean, hosting numerous gas and condensate fields. These fields consist of a combination of structural and stratigraphic traps with sandstone reservoirs from the Miocene and Pliocene epochs, where turbidite hydrocarbon reservoirs with substantial hydrocarbon volumes are found. This article focuses on the Miocene gas reservoir of the Abu Madi formation in the Abu Qir and Yazzi offshore gas fields, located in the West Nile Delta basin north of Alexandria, Egypt. The study integrates recorded formation pressure testing points and production history of 6 wells that intervened Abu Madi Formation to identify isolated compartments that do not contribute to field production. The analysis revealed three gas reservoirs. The first reservoir, in the North Abu Qir field, has experienced long-term production, leading to pressure depletion and aquifer sweep, reducing the depth of the gas-water contact. The second reservoir, in the Yazzi field, shows some pressure depletion affected by the first reservoir production, but its gas-water contact remains at its initial depth. The third reservoir, in the eastern compartment, exhibits partial pressure depletion. The conceptual model proposes two main concepts for the relationship between the three gas reservoirs. The first concept involves the local structural framework, where the Abu Madi formation trap consists of multiple anticlines, with spill points playing a crucial role in filling hydrocarbons in these anticlines with the same water contact. The second concept considers the impact of stratigraphic facies changes and heterogeneity on transmissibility and permeability, affecting volumetric recovery efficiency. The information was compiled into a 3D reservoir simulation model, concluding that the remaining hydrocarbons must be redistributed. This opens the possibility of hydrocarbon accumulation in partially isolated compartments that need development. Additionally, the interpretation of recorded formation pressure testing points gradients was essential in identifying hydrocarbon fluid types, revealing an oil rim in the gas reservoir in the eastern compartment of the Abu Qir gas field, which altered the well development strategy in that area. 

Unveiling Mediterranean Heterogenous Gas Reservoirs Through Pressure Points Analysis and Reservoir Simulation Modelling


 Reservoir simulation is required to aid in the decision-making for high-impact projects. It is a culmination of geophysical, geological, petrophysical, and engineering assessments of sparse, uncertain, and expensive data. History matching is a process of elevating trust in numerical models as they are calibrated to mimic the behaviour of the real-life asset. Traditional history matching relies on direct parameter assignment based on flat files used as input to the reservoir simulator. This enables a convenient method for the perturbation of uncertain parameters and their value assignments during the history matching process. Given the nature of the input files, the scope for uncertainty parameters is limited to the original petrophysical properties, their derived simulation properties in a specified group of grid blocks, and occasionally extended to include fluid and multiphase flow properties. However, there are key influential model-building steps prior to reservoir simulation related to data interpretation. These steps control not only the values of petrophysical properties but also their spatial correlation, cross-correlation, and variability. The limitation in the scope for parameterization adds bias to the model calibration process, hence negatively impacting its outcome.
 In an era where ML/AI algorithms are shaping data interpretation methods, key modelling decisions can be revisited to realize the maximum value of subsurface data. However, a framework is required whereby these important model-building steps are captured in history matching to eliminate bias and ensure the geological consistency of the subsurface model during and after history matching. This paper demonstrates a liberated workflow to calculate the recommended parameters that achieve the minimum mismatch score. The workflow is executed through a cloud platform offering compute elasticity to expedite the history matching workflow. It is composed of three main steps. The first step is data loading, where simulation results and parameters are extracted from the submitted ensemble(s).
 Meanwhile, the second step involves data preparation and cleaning. Wells devoid of data are removed, and scaled metrics are created to calculate the mismatch score. The simulation ID then groups the data to get a field-level aggregation. The now aggregated and cleaned simulation results are merged with the parameters list to create the input dataset to the final step, where several machine learning models are trained and evaluated in parallel. The data is split into training and testing datasets. The target variable is the mismatch score, as the models are trying to predict the mismatch for a given set of parameters. Supervised learning regression algorithms were used. The best-performing ones were found to be random forest and gradient boosted trees. After fine-tuning the machine learning models and evaluating them based on their coefficient of determination (R2 score), the best fitting model is used to calculate the optimized parameters. This happens iteratively by generating new series of parameters within a range and using the machine learning model to predict the mismatch for each until the lowest mismatch is found. The parameters resulting in the minimum mismatch are the recommended parameters.
 This workflow is implemented on a simulation model built for a mature gas condensate field in the Mediterranean of Egypt. The field comprises three anticlines with a spill-fill Petroleum system, where the majority of the wells are in one of the anticlines. In contrast, the other anticlines have few wells and are candidates for appraisal. Moreover, there is high uncertainty in the sand distribution and reservoir properties, spill points depth, depletion, and observing an explained phenomenon of a sustainable gas water contact in the new anticline even after 30 years of production from the old Anticline. This uncertainty in the understand of the relation between the two anticlines makes the selection of the drilling locations a challenge. To Assess remaining reservoir volumes and identify potential infill targets, we used the ML to study all the uncertainties combinations in a full-loop approach from static to dynamic model and generate multiple representations that honour the geological understanding.
 The cloud-based Agile reservoir modeling approach enriched with ML / AI algorithms enabled us to generate Multiple realizations that match 30 years of historical production and pressure profiles capturing many possible combinations of uncertain geological parameters and concepts. In addition, several forecast scenarios for 3 new appraisal wells were optimized based on the ensemble of history matched models minimizing the risk of drilling dry wells. In addition to going through the work process and results, this paper highlights the method's practical effectiveness and common issues in practical application.
 The use of the cloud-based technology had a great cost saving and efficiency improvements, for example giving the existing on-premises Infrastructure would take 1-2 years to achieve the same results that was achieved in 1-2 months and cost saving around 1 million dollars in cluster hardware purchase. Moreover, Cloud based technology enable collaborative, iterative working styles for integrated teams and access to scalable technologies that are developed on cloud only.

Cloud-Based Agile Reservoir Modelling Enriched with Machine Learning Improved the Opportunity Identification in a Mature Gas Condensate

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Cloud-Based Agile Reservoir Modelling Enriched with Machine Learning Improved the Opportunity Identification in a Mature Gas Condensate