Multiresolution Wavelet Analysis for Improved Reservoir Description

In current practice history matching is often performed on upscaled reservoir models without updating the original geological model. Taking into account new production data in an existing model is often problematic because of the lack of appropriate techniques. This problem becomes crucial when using geostatistical models. In such a case, the integration of new static and dynamic constraints requires the modification of an initial realization without destroying the initial history match and the overall geostatistical properties. This paper presents a new methodology based on advanced history matching techniques for updating dynamically 3D stochastic reservoir models to account for new production data. The proposed approach is based on the gradual deformation method. It allows local or global smooth transformations of the model while conserving the overall statistical characteristics. History matching is performed by coupling this method with an optimization process which integrates geostatistical modeling, upscaling, and fluid flow simulation in the same loop. This approach provides great flexibility in history matching various types of production data. In addition, inversion parameters can be selected throughout the entire modeling loop, from the facies spatial distribution, to petrochemical parameters and fluid flow parameters. Finally, local transformations may be performed to account for new wells or to update the reservoir model with new dynamic information. A successful application to a 3D stochastic reservoir model inspired from a real field is presented. The first step was to perform a history match using the production history available from an initial group of production wells. Then, new dynamic information is acquired from infill wells and over one year of additional production data. The heterogeneity distribution is locally modified by gradual deformation to update the model with this new information. The benefit of the proposed approach for improving the model characterization and reducing uncertainty on production forecasts is demonstrated.

Producing hydrocarbon from Shale plays has attracted much attention in the recent years. Advances in horizontal drilling and multi-stage hydraulic fracturing have made shale reservoirs a focal point for many operators. Our understanding of the complexity of the flow mechanism in the natural fracture and its coupling with the matrix and the induced fracture, impact of geomechanical parameters and optimum design of hydraulic fractures has not necessarily kept up with our interest in these prolific and hydrocarbon rich formations. In this paper we discuss using a new and completely different approach to modeling, history matching, forecasting and analyzing oil and gas production in shale reservoirs. In this new approach instead of imposing our understanding of the flow mechanism and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. In other words, by carefully listening to the data from wells and the reservoir we developed a data driven model and history match the production process from Shale reservoirs. The history matched model is used to forecast future production from the field and to assist in planning field development strategies. We use the last several months of production history as blind data to validate the model that is developed. This is a unique and innovative use of pattern recognition capabilities of artificial intelligence and data mining as a workflow to build a full field reservoir model for forecasting and analysis of oil and gas production from shale formations. Examples of three case studies in Lower Huron and New Albany shale formations (gas producing) and Bakken Shale (oil producing) is presented in this paper.

The Marcellus Shale play has attracted much attention in recent years. Our full understanding of the complexities of the flow mechanism in matrix, sorption process and flow behavior in complex fracture system (natural and hydraulic) still has a long way to go in this prolific and hydrocarbon rich formation. In this paper, we present and discuss a novel approach to modeling and history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining & pattern recognition technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, and hydraulic fracturing data to force their will on our model and determine its behavior. The uniqueness of this technique is that it incorporates the so-called "hard data" directly into the reservoir model, such that the model can be used to optimize the hydraulic fracture process. The "hard data" refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. The study focuses on part of Marcellus shale including 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full-field history matching process was completed successfully. Artificial Intelligence (AI)-based model proved its capability in capturing the production behavior with acceptable accuracy for individual wells and for the entire field.

The paper describes reservoir management challenges during the first 5 years of the production from the Terra Nova oil field, located offshore Newfoundland and Labrador, Canada. The purpose of the paper is to share lessons learned in the appraisal strategy, data monitoring, subsurface modeling and production management of this subsea/ floating, production, storage, offloading (FPSO) development. The material covers appraisal history, reservoir description, production history, reservoir monitoring, reservoir management, lessons learned and future challenges. The paper will discuss the need for a balanced approach regarding development and delineation of the resources. Since there is no gas export from Terra Nova or any of the Grand Banks fields, surplus gas has to be re-injected. The Terra Nova depletion plan requires that the production from the water injection region and the gas flood regions be balanced for optimum pressure management. The relative richness of the injection gas from the Terra Nova production process has the potential to create miscible gas-oil conditions. We will discuss how slim tube experiments and compositional tracking of the fluids have demonstrated benefits to the oil recovery by operating the gasflood at a reasonable pressure. The Terra Nova field lies not only in a harsh environment but also in an immature, low infrastructure area. This has led to additional challenges regarding reservoir management and future development. Production behavior and history matching have shown that seismically defined fault blocks are transmissible rather than sealing. The presence of leaky faults is beneficial for oil recovery, but presents challenges for precise reservoir simulation modeling and history matching. Terra Nova has extensive amount of data from bottom hole pressure gauges, wireline formation pressures (e.g. RFT/RCI), water cut trends GOR trends, gas tracers, etc. The paper discusses how these data have been integrated in the reservoir management process. Various history matching approaches will be discussed; both manual history matching and assisted techniques (experimental design). This is to the best of our knowledge the first SPE paper from the Grand Banks oil province that describes integrated reservoir management challenges particular to this region.

Summary Accurate characterization of sub-surface oil reservoirs is an essential prerequisite to the design and implementation of enhanced oil recovery (EOR) scenarios. Specifically, in reservoir characterization, integrating static and dynamic data into reservoir models to construct accurate and realistic models has received considerable attention. Unlike most of the conventional geostatistical approaches of integrating data into reservoir models that are based on semi-variograms (two-point statistics) as a measure of spatial connectivity, a complete multiple-point (MP) statistic framework is presented in this paper. In contrast to two-point statistic methods, MP statistics-based methods are capable of reproducing curvilinear geological structures. The algorithm starts with extracting MP statistics from training images (TI) using an optimal spatial template. After collecting different patterns and building the MP histogram, the pattern reproduction process commences. This process begins from data locations and then grows to fill the whole reservoir domain. The algorithm accounts for three main practical issues: uncertainty in geological scenarios, scanning template and non-stationarity. The MP statistics algorithm (growthsim) is capable of integrating data from multiple data sources. Among these data types is dynamic data or flow history. The conventional approach to integrate production information into reservoir models is by iterative perturbation of the reservoir model until the production history of the reservoir is matched. Iterative methods have been applied till date to random fields that are completely characterized by a two-point co-variance function. In contrast, this paper presents a forward modelling approach that investigates history matching within a MP modelling framework. A novel technique implemented in this research is based on the merging of MPs inferred from history matched and geological models. Pattern growth is performed subsequently by sampling from the merged MP histograms. History matched models using the presented approach show an excellent agreement with underlying geological descriptions and match production history.

The risk of economic failure of infill drilling campaigns increases with the increasing maturity of an oil field. The increased risk is due to the lower remaining movable oil in the late stages of a field's life compared to earlier phases. Despite the additional information owing to drilling of wells and production history, substantial geological and dynamic uncertainties remain. In this paper, an approach is presented for selecting an infill well portfolio of 10 wells from a total of 96 that explicitly accounts for these uncertainties. The geological uncertainty can be exposed by generating a multitude of geological models conditioned to known log/correlation data. These models need to be classified and a subset of representative models extracted that in turn are calibrated (history matched) to past field performance and then used for forecasting. Calibration using a suitable ensemble of representative geological models aims to maintain geological diversity while reducing the error to historical data through an objective function. Probabilistic Property Maps can then be generated to identify potential infill well locations. Probabilistic maps offer a major improvement compared to selection of infill well locations using a single geological realization. For mature assets, forecasting requires simulation of incremental oil recovery since infill wells represent incremental projects over a base case. Using an appropriate ensemble of models allows probabilistic representation of the incremental oil production and associated economics. First, individual infill well performances are forecasted. Next, the individual infill well locations are evaluated taking the history matching error into account and using utility theory to cover the risk adverseness attitude of the company. Doing so enables selection of an infill well portfolio under uncertainty and leads to a selection of well locations carrying lower risk compared to a selection of locations neglecting history match error and/or disregarding the risk attitude of a company.

SummaryRecently, we have developed two computationally efficient data-driven models—interwell numerical simulation model (INSIM) and INSIM with front tracking (INSIM-FT)—for history matching, prediction, characterization, and optimization of waterflooding reservoirs. Then, stemming from the INSIM family, we derived a new data-driven model referred to as INSIM with flow-path tracking (FPT), for more-accurate interwell connectivity calculations, dynamic flow-path tracking, and waterflooding predictions. The model is a connection-based simulation model that is developed on the basis of a two-phase-flow material-balance equation. With the new model, we can characterize a reservoir by history matching the historical well flow-rate data without the detailed petrophysical properties of the reservoir. In INSIM-FPT, we provide an automatic and systematic workflow that incorporates Delaunay triangulation and imaginary wells to construct the model connection map. We apply a modified depth-first searching method to track all influential flow paths between an injector/producer pair for more-accurate calculations of dynamic allocation factors and control pore volumes (PVs). In addition, we provide a method to visualize a saturation field for a history-matched INSIM-FPT model. On the basis of the saturation map, we design a workflow to evaluate possible drilling locations and future performance of infill wells.For application, we create a synthetic reservoir with two different scenarios to demonstrate the reliability of INSIM-FPT. The results show that the dynamic allocation factors and control PVs between injector/producer pairs in the history-matched INSIM-FPT models are consistent with those obtained from the true streamline simulation model. Furthermore, the oil-saturation field generated with INSIM-FPT reasonably matches that obtained with the true model. It shows that the future predictions of infill wells on the basis of history-matched INSIM-FPT models are reasonably accurate but can be improved if more observed data are collected from near the planned infill wells. We also test a large-scale field problem with 65 wells, which shows INSIM-FPT can reasonably match and predict the field data.

SummaryWe present the development and field application of a workflow for multiscale reservoir-model calibration that seamlessly integrates production data into the reservoir description from the facies to the grid-cell scale. To start with, the permeability field is parameterized using a novel grid-connectivity-based transformation basis that can be applied with any model geometry, including unstructured and corner-point grids. The parameterization basis functions emerge from spectral decomposition of the grid-connectivity Laplacian and are related to the structural harmonics of the grid. To reconcile data with model resolution during history matching, we first use the coarsest-scale basis functions to identify the large-scale variability. Additional smaller-scale basis elements are then adaptively incorporated to successively refine the model to a level supported by data resolution. During refinement, the inclusion of more detailed basis functions into the parameterization is determined by generic modal frequency when the prior model is unavailable or by using prior information when available. In the final step of the workflow, a streamline-based inversion is performed to locally adjust the reservoir model at grid-cell resolution along preferential-flow paths defined during the coarser-scale parameterization.We demonstrate the suitability and effectiveness of the developed workflow through application to an offshore turbidite reservoir with frequent well intervention, including shut-ins and recompletions. The static model has over 300,000 cells, a complex channelized interpretation with faults, four injector/producer pairs with deviated wells, and over eight years of production history, including water cut and pressure data. The grid-connectivity-based parameterization effectively updates the prior regional permeability at scales and in locations warranted by the data, while preserving the geologic continuity and avoiding ad hoc redefinition of regions given the sparse well pattern. The multiscale calibrated-permeability field indicates flow communication previously unrecognized in static geologic interpretation or manual history matching.

Using commercial numerical reservoir simulators to build a full field reservoir model and simultaneously history match multiple dynamic variables for a highly complex, offshore mature field in Malaysia, had proven to be challenging, manpower intensive, highly expensive, and not very successful. This field includes almost two hundred wells that have been completed in more than 60 different, non-continuous reservoir layers. The field has been producing oil, gas and water for decades. The objective of this article is to demonstrate how Artificial Intelligence (AI) and Machine Learning is used to build a purely data-driven reservoir simulation model that successfully history match all the dynamic variables for all the wells in this field and subsequently used for production forecast. The model has been validated in space and time. The AI and Machine Learning technology that was used to build the dynamic reservoir simulation and modeling is called spatio-temporal learning. Spatio-temporal learning is a machine-learning algorithm specifically developed for data-driven modeling of the physics of fluid flow through porous media. Spatio-temporal learning is used in the context of Deconvolutional Neural Networks. In this article Spatio-temporal Learning and Deconvolutional Neural Networks will be explained. This new technology is the result of more than 20 years of research and development in the application of AI and Machine Learning in reservoir modeling. This technology develops a coupled reservoir and wellbore model that for this particular oil & gas field in Malaysia uses choke setting, well-head pressure and well-head temperature as input and simultaneously history matches Oil production, GOR, WC, reservoir pressure, and water saturation for more than a hundred wells through a completely automated process. Once the data-driven reservoir model is developed and history matched, it is blind validated in space and time in order to establish a reliable and robust reservoir model to be used for decision making purposes and opportunity generation to maximise the field value. The concepts and the methodology of history match of multiple wells, individual offshore platforms, and the entire field will be presented in this article along with the results of blind validation and production forecasting. Results of using this model to perform uncertainty quantification will also be presented. A case study of a highly complex mature field with large number of wells and years of production has been used to be studied and simulated by this data-driven approach. Time, efforts, and resources required for the development of the dynamic reservoir simulation models using AI and Machine Learning is considerably less than time and resources required using the commercial numerical simulators. It is validated that the TDM developed model can make very reasonable prediction of field behavior and production from the existing wells based on modification of operational constraints and can be a prudent complementary tool to conventional numerical simulators for such complex assets.

As the role of reservoir flow simulation increasingly impacts existing operations and field development decisions, it follows that rigor, fitness and consistency should be imposed on the calibration of reservoir flow models to dynamic data through history matching. Although a wealth of history matching techniques exist in the petroleum literature that propose novel algorithms or share case studies, seldom does the content guide the modeler in fit-for-purpose reservoir model calibration for an operating asset. To evaluate the applicability of these diverse techniques against standards required for reservoir management, an internal study was performed to benchmark four assisted history matching (AHM) techniques commonly promoted in the oil and gas industry. The techniques were vetted against a comprehensive suite of modeling requirements for multiple asset classes, integrating a variety of historical dynamic data types through the calibration of reservoir properties that control flow behavior from the field to inter-well scale. The methods benchmarked were: (1) Design of Experiments (DoE)-based, (2) Ensemble Kalman Filter and Ensemble Smoother, (3) Genetic Algorithm and (4) Generalized Travel Time Inversion. This manuscript focuses solely on the DoE-based technique.In order to consistently benchmark the techniques, a set of standards was defined against which each was evaluated to determine its suitability for widespread history matching applications. The standards involve: the capacity to parameterize (and therefore calibrate) a diversity of reservoir flow model attributes, the capacity to integrate different types of dynamic data, the level of independence from the flow simulator and the capability to provide probabilistic outcomes for predictive uncertainty assessment. Of the four techniques, the DoE-based approach uniquely satisfied all requirements. Its history matching workflow has the flexibility to incorporate any form of reservoir model parameter and to assimilate a history matching error metric for any individual or group of historical data types; therefore, benchmarking established DoE-based techniques as unambiguously the most compliant with generic asset modeling requirements. The approach was also identified as the most straightforward, both theoretically and in practical computation, and therefore applicable to the broadest range of practitioners. Perhaps most importantly, the approach demonstrated the capacity for accurate quantification of uncertainty (or non-uniqueness) in reservoir quality resulting from an exhaustive, although approximate, exploration of model parameter space and the associated history matching error metric(s).This manuscript compiles the results and insights gained from benchmarking of the DoE-based techniques through the proposal of a comprehensive assisted history matching workflow. The workflow is designed for generality while providing best practices that guide the modeler in fit-for-purpose application. Limitations of the workflow are also recognized. Key components include: selection and screening of calibration parameters based on statistical significance, development of surrogate models to characterize the relationship between parameters and the simulated historical data being integrated into the reservoir model, use of the surrogate models for exhaustive yet efficient exploration of parameter space to identify (non-unique) history matched models, and (deterministic or probabilistic) discrete reservoir model selection for use in forecast-based decision making. Each step of the AHM workflow is presented from a conceptual and applied perspective, and field applications are provided to demonstrate key concepts. Although the applications presented include two deepwater Gulf of Mexico assets, the workflow and insights provided are developed from benchmarking across a diverse suite of asset types.

An updated geological and dynamic model for a giant Middle East carbonate reservoir was constructed and history matched with the objective of creating an alternative model which is capable of replicating the reservoir production mechanisms and improving predictability, allowing optimizing the field development plan and water injection strategy. Giant Middle East carbonate fields often have long production history and exhibit high reservoir heterogeneity. It is always challenging to get a robust history matched model aligned with geological concepts and dynamic behavior understanding. The objective of this paper is to present an improved and integrated reservoir characterization, modeling and history matching procedure for a giant Lower Cretaceous carbonate reservoir in the Middle East. The applied workflow integrates all available geological data (stratigraphy, depositional facies, and diagenesis), petrophysical data (RCA and minipermeameter data, Petrophysical Group definition, cut-off definition) and the extensive database of dynamic data (long production history, well test, RST, open-hole log saturation over more than 40 years of development drilling, and MICP). The process was initiated with the reservoir review by means of a fully integrated study that allowed having better understanding of the reservoir behavior and production mechanisms. The key heterogeneities (high permeability and intra-dense layers) which control the flow behavior were identified during this process. Geological trend maps were generated to control the distribution of high permeability and intra-dense in the model. Well test data, open-hole logs from development wells and time-lapse saturation logs from observation wells were used to calibrate the trend and permeability log data. A phenomenological model was constructed to test the main factors impacting the production mechanism as identified during the reservoir review. Multiple iterations were performed between the static and dynamic models in a way that allowed a quick and efficient work that is consistent with all disciplines assumptions. Such continuous loop between the dynamic and geological models, with focus on the geological heterogeneities driving the dynamic reservoir behavior, has led to a more robust model capable of replicate the production mechanisms, which represents a major improvement compared to previous model in term of predictability.

Reservoir modeling is a critical step in the planning and development of oil fields. Before a reservoir model can be accepted for forecasting future production, the model has to be updated with historical production data. This process is called history matching. History matching requires computer flow simulation, which is very time‐consuming. As a result, only a small number of simulation runs are conducted and the history‐matching results are normally unsatisfactory. This is particularly evident when the reservoir has a long production history and the quality of production data is poor. The inadequacy of the history‐matching results frequently leads to high uncertainty of production forecasting. To enhance the quality of the history‐matching results and improve the confidence of production forecasts, we introduce a methodology using genetic programming (GP) to construct proxies for reservoir simulators. Acting as surrogates for the computer simulators, the “cheap” GP proxies can evaluate a large number (millions) of reservoir models within a very short time frame. With such a large sampling size, the reservoir history‐matching results are more informative and the production forecasts are more reliable than those based on a small number of simulation models. We have developed a workflow which incorporates the two GP proxies into the history matching and production forecast process. Additionally, we conducted a case study to demonstrate the effectiveness of this approach. The study has revealed useful reservoir information and delivered more reliable production forecasts. All of these were accomplished without introducing new computer simulation runs.

Structural, stratigraphic, and petrophysical uncertainties result in a wide range of geologic interpretations. For fields with long production and pressure history, 3D-dynamic simulations have been very useful in providing feedback to geologic modelers, which results in improved static models. The Chuchupa Field has produced 1.9 Tscf of dry gas, or approximately 40% of the Original Gas in Place (OGIP). At the time of this study, 3 new horizontal wells were being planned, and new gas sales agreements were being considered. We developed a dynamic workflow to create a range of probabilistic simulation models to forecast dry-gas production under several production scenarios in the Chuchupa field. Recent seismic re-interpretation, a new stratigraphic study and a revision of the petrophysical model resulted in new probabilistic static models for the field. While these static models were being built, a parallel numerical simulation study was conducted to determine the range of OGIP values that could be successfully history-matched. Nine numerical reservoir models were generated by applying pore volume multipliers to the prior-generation reservoir model, yielding a range of OGIP from 3.8 to 6.6 Tscf. We attempted to history match each of these nine models by using an optimization routine to adjust aquifer support, vertical transmissibility across a potential seal, and rock compressibility. The optimization routine proved to be a very useful and efficient tool to attain good quality history matches in short periods of time. Good matches were obtained for models with OGIP ranging from 4.3 to 5.8 Tscf. Based on this information, the geologic modelers revised petrophysical parameters and generated 27 static models. These models encompassed 3 structural interpretations, 3 porosity distributions, and 3 possible positions of gas/water contact (GWC). From experimental design we obtained the P10, P50, and P90 OGIP values of 4.1, 4.7, and 5.3 Tscf respectively. We scaled up and built reservoir simulation models for 8 geologic interpretations to represent the range of OGIP and reservoir geometries. Again, these models were history matched using the optimization routine. The match parameters were static well pressures and the absence of water production. Six out of the eight the models could be satisfactorily history-matched with reasonable adjustments to aquifer strength, vertical transmissibility, and rock compressibility. The OGIP range for these models was 4.1 to 5.6 Tscf. We selected 3 models to forecast future gas production. These models match the P10, P50, and P90 OGIP values determined in the probabilistic static model, and combine the low, mid, and high structures, porosity and Swi distributions, and the range of GWC positions. We also calibrated the various models with historical bottomhole and tubinghead flowing pressures, and coupled the reservoir model with a network consisting of surface lines and equipment; pipelines from two platforms to the onshore sale-point station; and multi-stage compression to 1,215 psia. The model is currently used to evaluate various production and market scenarios.

Reservoir model history matching is a complex, time-consuming, and resource intensive process that needs to be carried out carefully for building reliable predictive tools to manage Oil & Gas assets. Reservoir models encompass detailed geological description representing subsurface heterogeneities that influence its dynamics. To intelligently manage and preserve the complexity of the reservoir models, an artificial intelligence, Progressive-Recursive Self-Organizing Maps (PR-SOM), algorithm was developed. PR-SOM is an unsupervised artificial intelligence neural network algorithm that classifies the reservoir grid cells into progressive reservoir parameters to identify similarly adjoining regions. The algorithm explores and identifies model geo-bodies with similarities and dissimilarities in a progressive and recursive manner. This allows history matching to be conducted on much smaller subsets of the reservoir model of similar geological features. In this work, an artificial intelligence (AI) algorithm was applied, first, to guide the reservoir-wide history match processes. Next, the algorithm was applied to fine-tune well performance using information form well testing and historical data. The algorithm uses both static properties (permeability, rock quality indices, porosity, flow zonation … etc.) and dynamic properties (pressure or saturation) to construct similarities matrix. The results show that the clusters’s growth is progressive, controlled and quality assured by accounting for the controlling reservoir parameters. The number of mapped regions (clusters) is determined by optimizing the similarity matrix recursively. The quality of the global reservoir history match shows the effectiveness of the algorithm, better quality matching for historical production data, and fewer iterations (i.e. less simulation runs). The process is repeated to calibrate the reservoir model near wellbores by limiting the AI algorithm to only the drainage regions seen from well tests and historical data. The results show that employed AI-guided history matching revealed similarities and dissimilarities in the reservoir model. That not only enhance field and well match, but also allowed us to maintain the heterogeneity contrasts inherited from the Earth model. The advanced algorithm was successfully used to assess the extent of geological heterogeneity and its impact on reservoir dynamics, to enhance history match quality, minimize human interaction, and to reduce computational requirements.

Probabilistic production forecasting at Tengiz is largely driven by reservoir uncertainty. Reservoir uncertainty is most effectively synthesized and quantified through simulation modeling. Early in the construction of a new Tengiz dynamic model, fundamental reservoir uncertainties were identified and evaluated. This allowed for model ‘building blocks’ to be developed with different characterizations to encompass key uncertainties. Key uncertainties, which can significantly impact future production under primary depletion and sour gas injection, have been described. These include typical uncertainties such as porosity, irreducible water saturation, hydrocarbon fluid properties, oil-water contact levels, rock compressibility, geologic baffles, and relative permeability. Unique uncertainties specific to Tengiz include geometry and density of the natural fractures, and reservoir heterogeneity. Considerable production history and a large reservoir surveillance database provided input for rigorously characterizing and subsequently validating the range of each uncertainty. After ranges were established, appropriate model realizations were created. A wide range of reservoir models were obtained by selecting combinations of high/mid/low realizations for each uncertainty. Using experimental design (ED), reservoir simulations were conducted to test uncertainty ranges against field history. A quantitative history match and statistical analysis were developed to objectively judge the appropriateness of uncertainty values. Uncertainties with the largest overall impact on the history match are: fracture density, platform horizontal permeability, compressibility, and platform heterogeneity. This case study demonstrates how analysis of reservoir uncertainties can be: (1) captured in static and dynamic reservoir models and (2) validated through ED and quantitative history matching. This study employs state-of-the-art technologies to evaluate model uncertainties of a giant carbonate reservoir undergoing both depletion and miscible gas drives. The range of reservoir models subsequently developed will be of great value in creating robust probabilistic reservoir forecasts to optimize field operation and future development.