Full-field Simulation Model Research Articles

Incident-Field Estimation for Active Cloaking

Active-scattering-cancellation techniques provide a means of achieving electromagnetic cloaking without dealing with passivity-based design and performance limitations. However, a major drawback is that the cloak must be adjusted to accommodate a specific set of illumination conditions. Until recently, configuration has been performed with these conditions known a priori. Although adequate for initial validation, this presents a major obstacle to the development of a practical device that must contend with an unknown and dynamic electromagnetic environment. As a solution, this paper presents the development of an estimation algorithm that uses field measurements, sampled within the vicinity of the object to be cloaked, to deduce incident-wave properties. This information can then be used to adaptively reconfigure the cloak without any prior knowledge of its surroundings. In addition, the use of iterative solvers is avoided in estimator development, sidestepping convergence issues that afflict previous designs while reducing run time. After presenting the development process and operation principles, performance evaluation is conducted by using the algorithm to estimate the incident angle, signal magnitude, and phase offset of an incident wave impinging on either a circular or polygonal quasi-two-dimensional conductive target. Input data sets are initially provided via full-field simulation models and later supplanted by real-world field measurements.

Impact of Relaxation of Low-Sulfate Seawater Parameters on Scaling Risk

SummaryInjection of low-sulfate seawater (LSSW) instead of untreated full-sulfate seawater (FSSW) is widely used to mitigate barium sulfate scaling risk at the production wells. LSSW injection may no longer be required when the barium concentrations in the produced water drop below a certain threshold. Such a trigger value could be estimated from the barium sulfate precipitation tendency. Relaxation of requirements for the sulfate reduction plant (SRP) can significantly reduce operational costs. This study investigates the impact of several parameters on the timing and degree of relaxation of the output sulfate concentration by the SRP. Finally, the optimal switching strategy is proposed for a field case.The strategy for switching from LSSW to FSSW (e.g., time and method; direct or gradual increase in the sulfate concentration) was initially investigated using generic 2D areal and vertical models. The sensitivity study included the impact of reservoir heterogeneity and the initial barium and sulfate ion concentrations. Findings were later applied on a full-field reservoir simulation model followed by a mineral scale prediction software to investigate the specific switching strategy for a field that has multiple wells and significantly more complex heterogeneity.The results show that barium concentrations in the formation brine affect the choice of switching time more than the output sulfate concentration produced by the SRP. The degree of heterogeneity around the producers also has a significant impact on the switching time. Another parameter is the contrast in the permeability between layers; higher contrast allows a longer period of coproduction of the scaling ions and thus delays the switching time. In the field case, switching to FSSW at early times allows higher consumption of barium ions because of its in-situ precipitation. Barium is no longer a limiting ion, and so a higher degree of deep reservoir precipitation reduces the requirement for prolonged LSSW injection. Another strategy is a gradual relaxation of LSSW output, which allows even earlier buildup of the injected sulfate concentration compared with the direct FSSW switch.The study investigates the reservoir parameters that affect sulfate relaxation of LSSW injection for a field. After the proposed workflow, the optimal relaxation strategy can be designed for other field cases.

Dynamic calibration of the Shaikan Jurassic full-field fractured reservoir model through single-well DST and multi-well interference discrete fracture network simulation

The hydraulic behaviour of the fractures in a fractured carbonate reservoir is a function of fracture intensity, aperture, intrinsic permeability, length, height and orientation, all of which influence the scale of connectivity and ultimately storage, productivity and reserves. If a geologically realistic fracture model is not appropriately incorporated into upscaled fracture properties for a dynamic simulation, it may still be possible to match a short production history, but calculations of field-wide fracture pore volumes and forecasts of future reservoir development may be poor and uncertain. To accurately represent the fractures, discrete fracture network (DFN) models were built and used to constrain fracture geometries and their hydraulic properties for use in forecasting, field development options and uncertainty characterization. The workflow illustrated in this paper shows how a DFN may be validated and calibrated through the simulation of transient bottom hole pressures from individual drill stem tests and pressure interference data, followed by upscaling to a full-field dynamic simulation model. This DFN-to-simulation workflow, applicable to most conventional fractured reservoirs, successfully matched reservoir pressure history for the field as a whole and for individual wells without having to locally modify any of the upscaled fracture properties around the wells. Sensitivity analysis identified key fracture drivers having the greatest impact upon the history match, and these were combined to produce history matched Low and High Case models. Production forecasts for the Low, Base and High Cases were used to predict reserves, manage risk and optimize the field development plan. Supplementary material : Supplementary figures are available at https://doi.org/10.6084/m9.figshare.c.5001203 Thematic collection: This article is part of the The Geology of Fractured Reservoirs collection available at: https://www.lyellcollection.org/cc/the-geology-of-fractured-reservoirs

Simulation of Immiscible Water-Alternating-CO2 Flooding in the Liuhua Oilfield Offshore Guangdong, China

In this paper, the immiscible water-alternating-CO2 flooding process at the LH11-1 oilfield, offshore Guangdong Province, was firstly evaluated using full-field reservoir simulation models. Based on a 3D geological model and oil production history, 16 scenarios of water-alternating-CO2 injection operations with different water alternating gas (WAG) ratios and slug sizes, as well as continuous CO2 injection (Con-CO2) and primary depletion production (No-CO2) scenarios, have been simulated spanning 20 years. The results represent a significant improvement in oil recovery by CO2 WAG over both Con-CO2 and No-CO2 scenarios. The WAG ratio and slug size of water affect the efficiency of oil recovery and CO2 injection. The optimum operations are those with WAG ratios lower than 1:2, which have the higher ultimate oil recovery factor of 24%. Although WAG reduced the CO2 injection volume, the CO2 storage efficiency is still high, more than 84% of the injected CO2 was sequestered in the reservoir. Results indicate that the immiscible water-alternating-CO2 processes can be optimized to improve significantly the performance of pressure maintenance and oil recovery in offshore reef heavy-oil reservoirs significantly. The simulation results suggest that the LH11-1 field is a good candidate site for immiscible CO2 enhanced oil recovery and storage for the Guangdong carbon capture, utilization and storage (GDCCUS) project.

Uncertainty span for relative permeability and capillary pressure by varying wettability and spatial flow directions utilizing pore scale modelling

Relative permeability and capillary pressure are key properties within special core analysis and provide crucial information for full field simulation models. These properties are traditionally obtained by multi-phase flow experiments, however pore scale modelling has during the last decade shown to add significant information as well as being less time-consuming to obtain. Pore scale modelling has been performed by using the lattice-Boltzmann method directly on the digital rock models obtained by high resolution micro-CT images on end-trims available when plugs are prepared for traditional SCAL-experiments. These digital rock models map the pore-structure and are used for direct simulations of two-phase flow to relative permeability curves. Various types of wettability conditions are introduced by a wettability map that opens for local variations of wettability on the pore space at the pore level. Focus have been to distribute realistic wettabilities representative for the Norwegian Continental Shelf which is experiencing weakly-wetting conditions and no strong preference neither to water nor oil. Spanning a realistic wettability-map and enabling flow in three directions, a large amount of relative permeability curves is obtained. The resulting relative permeabilities hence estimate the uncertainty of the obtained flow properties on a spatial but specific pore structure with varying, but realistic wettabilities. The obtained relative permeability curves are compared with results obtained by traditional SCAL-analysis on similar core material from the Norwegian Continental Shelf. The results are also compared with the SCAL-model provided for full field simulations for the same field. The results from the pore scale simulations are within the uncertainty span of the SCAL models, mimic the traditional SCAL-experiments and shows that pore scale modelling can provide a time- and cost-effective tool to provide SCAL-models with uncertainties.

Augmented Artificial Intelligence Improves Data Analytics in Heavy-Oil Reservoirs

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 193650, “Augmented-Artificial-Intelligence Solutions for Heavy-Oil Reservoirs: Innovative Work Flows That Build From Smart Analytics, Machine Learning, and Expert-Based Systems,” by David Castineira, Xiang Zhai, and Hamed Darabi, Quantum Reservoir Impact Group, prepared for the 2018 SPE International Heavy Oil Conference and Exhibition, Kuwait City, Kuwait, 10–12 December. The paper has not been peer reviewed. Recently, many heavy-oil fields have seen exponentially higher volumes of data made available as a result of omnipresent connectivity. Existing data platforms have focused traditionally on solving the problem of data storage and access. The more-complex problem of true knowledge discovery and systematic value creation from the massive amount of data is less frequently addressed. The authors of this paper propose a novel work flow for the problem of building intelligent data analytics in heavy-oil fields. Introduction Optimal reservoir management for heavy-oil reservoirs requires systematic solutions that combine both engineering ability and advanced analytics. The authors believe that this requirement is addressed by what they call augmented artificial intelligence (AAI), a process inspired by the intelligence-amplification concept in which machine learning and human expertise are combined to improve solutions derived by systems that learn without any type of input from engineers or geoscientists. Practical deployment of AAI will involve automated work flows that use solid technical expertise and proven processes to transform field data into more-effective reservoir-management solutions. Even with rapid data-preprocessing solutions in place, developing an optimal reservoir-management framework for heavy-oil assets is inherently complex. Identifying key recovery obstacles (KROs) and field-development plans (FDPs) typically takes many months, involving a large team of experts and the construction of sophisticated full-field simulation models. The recommendation is that automated work flows and AAI solutions are combined to identify those KROs rapidly and prepare robust FDPs that increase production and optimize current operations. Perhaps the less-intuitive step in developing systematic solutions for heavy-oil fields is the process of developing a quantitative reservoir diagnostic framework. This process must build from big-data analytics platforms and an array of analytical, numerical, and empirical models combined to deliver a catalog of KROs affecting field performance. To this end, the entire historical set of well, field, and reservoir data must be processed and input into this diagnostics platform. Once the KROs are understood, the next step is to translate the diagnostics into detailed action plans in the field that can generate production, reserves, or capital-efficiency improvements. This paper aims to offer an alternative approach to traditional work flows that identify recovery obstacles and development opportunities in heavy-oil fields by labor-intensive solutions. In contrast, the authors propose a systematic framework that provides three key advantages: Execution time is fast, and an initial opportunity inventory can be generated. The user can choose from multiple algorithms and methods to customize the technology to unique field/reservoir complexities. The core algorithms are data-driven, integrate multidisciplinary data sets, and leave little room for the biases of the user, which allows for a consistent and repeatable analysis.

Aquifer Acceleration in Parallel Implicit Field-Scale Reservoir Simulation

Summary Grid coarsening outside of the areas of interest is a common method to reduce computational cost in reservoir simulation. Aquifer regions are candidates for grid coarsening. In this situation, upscaling is applied to the fine grid to generate coarse-grid flow properties. The efficacy of the approach can be judged easily by comparing the simulation results between the coarse-grid model and the fine-grid model. For many reservoirs in the Middle East bordered by active aquifers, transient water influx is an important recovery mechanism that needs to be modeled correctly. Our experience has shown that the standard grid coarsening and upscaling method do not produce correct results in this situation. Therefore, the objective of this work is to build a method that retains the fine-scale heterogeneities to accurately represent the water movement, but to significantly reduce the computational cost of the aquifer grids in the model. The new method can be viewed as a modified two-level multigrid (MTL-MG) or a specialized adaptation of the multiscale method. It makes use of the vertical-equilibrium (VE) concept in the fine-scale pressure reconstruction in which it is applicable. The method differs from the standard grid coarsening and upscaling method in which the coarse-grid properties are computed a priori. Instead, the fine-scale information is restricted to the coarse grid during Newton's iteration to represent the fine-scale flow behavior. Within the aquifer regions, each column of fine cells is coarsened vertically based on fine-scale z-transmissibility. A coarsened column may consist of a single amalgamated aquifer cell or multiple vertically disconnected aquifer cells separated by flow barriers. The pore volume (PV), compressibility, and lateral flow terms of the coarse cell are restricted from the fine-grid cells. The lateral connectivity within the aquifer regions and the one between the aquifer and the reservoir are honored, inclusive of the fine-scale description of faults, pinchouts, and null cells. Reservoir regions are not coarsened. Two alternatives exist for the fine-scale pressure reconstruction from the coarse-grid solution. The first method uses the VE concept. When VE applies, pressure variation can be analytically computed in the solution update step. Otherwise, the second method is to apply a 1D z-line solve for the fine-scale aquifer pressure from the coarse-grid solution. Simulation results for several examples are included to demonstrate the efficacy and efficiency of the method. We have applied the method to several Saudi Arabian complex full-field simulation models in which the transient aquifer water influx has been identified as a key factor. These models include dual-porosity/dual-permeability (DPDP) models, as well as models with faults and pinchouts in corner-point-geometry grids, for both history match and prediction period. The method is flexible and allows for the optional selection of aquifer regions to be coarsened, either only peripheral aquifers or both the peripheral and bottom aquifers. The new method gives nearly identical results compared with the original runs without coarsening, but with significant reduction in computer time or hardware cost. These results will be detailed in the paper.

Optimizing the Use of Miscible Injectant at the Greater Prudhoe Bay Fields

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 180420, “Optimizing the Use of Miscible Injectant at the Greater Prudhoe Bay Fields,” by S.X. Ning, SPE, B.S. Jhaveri, E.M. Fueg, SPE, G. Stechauner, J.L. Jemison, SPE, and T.A. Hoang, BP Exploration (Alaska), prepared for the 2016 SPE Western Regional Meeting, Anchorage, 23–26 May. The paper has not been peer reviewed. Miscible injectant (MI) has been used at Prudhoe Bay and its satellite fields for more than 30 years. This paper provides an overview of all enhanced-oil-recovery (EOR) projects in the Greater Prudhoe Bay (GPB) region and presents the process and methodology for MI allocation to these projects and to individual injection patterns. A new approach is used to determine the amount of MI allocated to each project on the basis of predicted marginal MI use per barrel of EOR oil. Introduction The MI-gas EOR process at Prudhoe Bay began in late 1982. In late 1986, the Central Gas Facility (CGF) began operation, enabling the first field-scale EOR project, the Prudhoe Bay Miscible Gas Project (PBMGP), to start in early 1987. Since then, the PBMGP has expanded to more than 170 injection patterns in the main field of Prudhoe Bay. Fig. 1 provides a layout of the oil fields on the North Slope of Alaska. The CGF is a gas-processing plant that makes natural-gas liquids (NGLs) for shipment in the Trans-Alaska Pipeline System and MI for EOR projects. The CGF currently processes an annual aver-age of approximately 7.5 billion scf/D of produced gas to generate approximately 40,000 STB/D of NGL and more than 200 million scf/D of MI. The feed gas comes from three different sources—solution gas from the produced oil, free reservoir gas from the gas cap, and returned MI (RMI) that was previously injected into the reservoir. The total amount of MI produced at the CGF is the sum of the fresh MI (which can be forecast by the GPB full-field simulation model) and the recaptured RMI (which can be forecast by COBRA and process modeling). COBRA is a full-field scale-up tool for predicting MI performance on the basis of type-pattern-simulation-model results. MI gas produced at the Prudhoe Bay CGF is a rich gas composed primarily of carbon dioxide (approximately 21%), methane (approximately 32%), ethane (20%), propane (25%), and a small amount of butane and heavier components (approximately 2–3%). Most of the GPB EOR projects are pattern floods that use a water-alternating-gas (WAG) injection to improve areal and vertical sweep efficiencies.

Ensemble machine learning: An untapped modeling paradigm for petroleum reservoir characterization

The successful applications of the conventional Computational Intelligence (CI) techniques and Hybrid Intelligent Systems (HIS) in petroleum reservoir characterization have been reported. However, these techniques are limited in their capability to handle a single hypothesis of a problem at a time. The major objective of the reservoir characterization process is to produce models that are robust enough to help improve the accuracy of the predictions of reservoir properties for use in full-field and large-scale simulation models. Research in CI continues to evolve new techniques and paradigms to meet this noble objective. It has been shown that there are uncertainties in the reservoir characterization process as well as the optimal choice of CI/HIS models parameters. The main challenge is to develop models that are capable of handling multiple hypotheses to reduce the uncertainties thereby ensuring optimal solutions. The ensemble machine learning paradigm has been established to tackle this challenge. This new machine learning technology has not been adequately explored in handling some of the petroleum engineering challenges. This paper rigorously reviews the concept of ensemble learning paradigm, presents successful applications outside petroleum engineering and the geosciences, discusses a few successful attempts in petroleum engineering and the geosciences, and concludes with some recommendations for the much-needed future applications.

Underground storage of carbon dioxide in the Harvey area, south-west Western Australia

Underground storage of carbon dioxide as a means of reducing atmospheric emissions of CO2 has been examined both theoretically and practically over the past decade. There is a large amount of research and field experience in the energy sector to show that underground storage of CO2 is feasible. The Global Carbon Capture and Storage Institute has identified 38i large-scale projects (operating and/or in the planning) with many companies currently sequestering tons of CO2 per year. This paper reports on static and dynamic modelling studies conducted on the Harvey structure, onshore Perth Basin, to assess the suitability of the Lesueur Sandstone in the Lower Lesueur Region of Western Australia as a potential geological CO2 sequestration site. The study area for this project within this structure covers 332 km2 and is located approximately 13 km north-west of the town of Harvey, south of Perth. The static and dynamic modelling investigated a suite of full field simulation models that cover a range of subsurface uncertainties providing confidence that the CO2 plume stays below 800 mTVDss (metres true vertical depth sub sea) within the storage complex for more than 1000 years after injection ceases. The results of the modelling show that it could be feasible to inject 800 000 tonnes CO2 per annum over 30 years into the storage complex. Modelling studies shows that the main factors controlling CO2 plume migration are trapped gas saturation and the solubility of CO2 in brine.

Optimization of a Large Polymer Flood With Full-Field Streamline Simulation

Summary A field-scale polymer flood has been in operation since early 2010 in a major oil field of the Sultanate of Oman. The project is composed of 27 mature waterflood patterns that were converted to polymer flood in 2010. Because a polymer-flood project has high chemical operating expenditure, optimization of a polymer flood requires continuous tracking of the mass of polymer injected per unit volume of incremental oil produced (relative to waterflood) for each polymer-flood pattern. To meet these objectives, a full-field streamline simulation model was built, was history matched, and is being used for optimizing the polymer flood. Full-field simulation allows the proper modeling of each pattern and their interactions with offset patterns. However, full-field simulations can be expensive, so we use a streamline-based simulator to run forecast scenarios in a reasonable computation time on reasonable hardware. Streamlines have the added benefit of determining the time-varying well-rate allocation factors per pattern, meaning that pattern-level diagnostics are relatively easy to compute and are based on the dynamic flow characteristics of the model. Computational efficiency and quantification of patterns have facilitated use of the model for routine well and reservoir-management decisions. We show that one can determine the effectiveness of the polymer flood on a pattern-by-pattern basis over the historical polymer-injection period with a standard oil-produced vs. polymer-injected ranking. In forecasting, we show how to quantify the incremental recovery caused by polymer, above base waterflood, on a pattern-by-pattern basis to facilitate optimization of polymer-flood patterns and more specifically to determine when to stop polymer injection and which new patterns to move polymer injection to.

E&P Notes (May 2015)

E&P Notes Lessons From the Aviation Industry on Automation Trent Jacobs, JPT Senior Technology Writer One of the defining features of the 21st century will undoubtedly be the changing relationship between humans and automated machines. The aviation industry has long been a champion of automation and in March, three experts from this field offered their insights to oil and gas professionals about how the technology continues to be qualified and adopted. The presentations were made in London at the SPE’s Drilling Systems Automation Technical Section (DSATS) and IADC Advanced Rig Technology symposium, which is held each year on the eve of the SPE/IADC Drilling Conference and Exhibition. Faster Recognition Stephen Rassenfoss, JPT Emerging Technology Senior Editor A project to predict how much carbon dioxide injected into an oil field is likely to remain there forever set off the US Department of Energy (DOE) on a search for faster data analysis methods. Speed matters because making projections going out as far as 1,000 years using a probabilistic model (Monte Carlo) will require long computing times, which are costly. DOE is trying to limit the total time by seeking quicker, lower-cost ways to generate the required data inputs by finding reasonably accurate methods that are faster than multiple processing runs of a full-field reservoir simulation model. Tough Tracers Stephen Rassenfoss, JPT Emerging Technology Senior Editor A new miniature pressure/temperature sensor comes packaged inside a tough small ball capable of traveling to the bottom of a well while drilling and returning with data on board. The outer shell of the ball allows electrical components printed on a circuit board to survive a trip in the drilling mud to the bottom of the well, survive a close encounter with the drill bit, and return with readings along the way that can be wirelessly downloaded. Creators of the balls are seeking a lower-cost alternative to logging tools located behind the drill bit. The tracer was created by a company started by Mengjiao Yu, an assistant professor of petroleum engineering at the University of Tulsa, with backing from Saudi Aramco. Corrosion Prediction Stephen Rassenfoss, JPT Emerging Technology Senior Editor There are oil fields in Saudi Arabia with many, long-lived wells and not enough inspection equipment to frequently check all the pipes for corrosion damage. “With a limited number of corrosion logging tools, we cannot cover all of them in 1 or 2 years,” said Mohammed AlAjmi, a production engineer for Saudi Aramco, who estimated that it would take 5 years with the available equipment in a presentation at the SPE Digital Energy Conference and Exhibition in The Woodlands, Texas. To reduce the risk of undetected corrosion damage inside and outside the casing, AlAjmi developed a method to predict which wells were at the highest risk of damage using data-driven models and artificial intelligence methods.

History Matching the Norne Full-Field Model Using an Iterative Ensemble Smoother

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 164902, ’History Matching of the Norne Full-Field Model Using an Iterative Ensemble Smoother,’ by Yan Chen, SPE, International Research Institute of Stavanger, and Dean S. Oliver, SPE, Uni Center for Integrated Petroleum Research, prepared for the 2013 EAGE Annual Conference and Exhibition/SPE Europec, London, 10-13 June. The paper has been peer reviewed and is scheduled for publication in the SPE Reservoir Evaluation & Engineering journal. This paper describes the application of the iterative ensemble smoother to the history matching of the Norne field in the North Sea, which has a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of identification of important variables, generation of an initial ensemble, plausibility of results, and efficiency of minimization. Introduction The Norne field produces from an oil and gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and nine injectors in the field. Water-alternating- gas injection is used as the depletion strategy. Production rates of oil, gas, and water of the 22 producers from 1997 to 2006 and repeat formation tester (RFT) pressure from 14 wells are available for model calibration. The full-field simulation model has 22 layers, each with dimension of 46×112 cells. The total number of active cell is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother [Levenberg- Marquardt ensemble randomized maximum likelihood (LM-EnRML)] is used for history matching the Norne full-field model using production data and RFT pressure. The model parameters that are updated include permeability, porosity, and net-to-gross ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from the LM-EnRML. The LM-EnRML is able to achieve improved data match compared with the manually history matched model after three iterations. Updates from the LM-EnRML do not introduce artifacts in the property fields as are produced in the manually history matched model. The automated workflow is also much less labor intensive than manual history matching. Ensemble-based methods use information from an ensemble of reservoir models to compute directions of change to the model. The most popular of these methods, the ensemble Kalman filter (EnKF), has been successfully applied on synthetic test cases in which well-bywell data match required many degrees of freedom.

History Matching of the Norne Full-Field Model With an Iterative Ensemble Smoother

Summary Although ensemble-based data-assimilation methods such as the ensemble Kalman filter (EnKF) and the ensemble smoother have been extensively used for the history matching of synthetic models, the number of applications of ensemble-based methods for history matching of field cases is extremely limited. In most of the published field cases in which the ensemble-based methods were used, the number of wells and the types of data to be matched were relatively small. As a result, it may not be clear to practitioners how a real history-matching study would be accomplished with ensemble-based methods. In this paper, we describe the application of the iterative ensemble smoother to the history matching of the Norne field, a North Sea field, with a moderately large number of wells, a variety of data types, and a relatively long production history. Particular attention is focused on the problems of the identification of important variables, the generation of an initial ensemble, the plausibility of results, and the efficiency of minimization. We also discuss the challenges encountered in the use of the ensemble-based method for complex-field case studies that are not typically encountered in synthetic cases. The Norne field produces from an oil-and-gas reservoir discovered in 1991 offshore Norway. The full-field model consists of four main fault blocks that are in partial communication and many internal faults with uncertain connectivity in each fault block. There have been 22 producers and 9 injectors in the field. Water-alternating-gas injection is used as the depletion strategy. Production rates of oil, gas, and water of 22 producers from 1997 to 2006 and repeat-formation-tester (RFT) pressure from 14 different wells are available for model calibration. The full-field simulation model has 22 layers, each with a dimension of 46 × 112 cells. The total number of active cells is approximately 45,000. The Levenberg-Marquardt form of the iterative ensemble smoother (LM-EnRML) is used for history matching. The model parameters that are updated include permeability, porosity, and net-to-gross (ntg) ratio at each gridblock; vertical transmissibility at each gridblock for six layers; transmissibility multipliers of 53 faults; endpoint water and gas relative permeability of four different reservoir zones; depth of water/oil contacts; and transmissibility multipliers between a few main fault blocks. The total number of model parameters is approximately 150,000. Distance-based localization is used to regularize the updates from LM-EnRML. LM-EnRML is able to achieve improved data match compared with the manually history-matched model after three iterations. Updates from LM-EnRML do not introduce artifacts in the property fields as in the manually history-matched model. The automated workflow is also much less labor-intensive than that for manual history matching.

Modeling pressure and saturation distribution in a CO2storage project using a Surrogate Reservoir Model (SRM)

Capturing carbon dioxide (CO2) from large point sources and depositing it in a geological formation is an efficient way of decreasing CO2 concentration in the atmosphere. A comprehensive study is required to perform a safe and efficient CO2 capture and storage (CCS) project. The study includes different steps, such as selecting proper underground storage and keeping track of CO2 behavior in the storage environment. Numerical reservoir simulators are the conventional tools used to implement such an analysis. The intricacy of simulating multiphase flow, having a large number of time steps required to study injection and post-injection periods of CO2 sequestration, a highly heterogeneous reservoir, a large number of wells, etc., will lead to a complicated reservoir model. A single realization for such a reservoir takes hours to run. Additionally, a thorough understanding of the CO2 sequestration process requires multiple realizations of the reservoir model. Consequently, using a conventional numerical simulator makes the computational cost of the analysis too high to be practical. In this paper, we examine the application of a relatively new technology, the Surrogate Reservoir Model (SRM), as an alternative tool to solve the aforementioned problems. SRM is a replica of full-field reservoir simulation models. It can generate outputs in a very short time with reasonable accuracy. These characteristics make SRM a unique tool in CO2 sequestration modeling. This paper proposes developing an SRM for a CO2 sequestration project ongoing in the SACROC unit to model pressure behavior and phase saturation distributions during different time steps of the CO2 storage process.

Characterizing and modeling natural fracture networks in a tight carbonate reservoir in the Middle East: A methodology

Fractured reservoirs are challenging to handle because of their high level of heterogeneity. In particular, natural fractures have a significant impact on well performance and water production. Therefore, understanding their significance through fracture characterization is helpful in well placement and field development. This paper presents a best practice methodology for building a 3D stochastic fracture model using a Middle Eastern tight carbonate field example. This model is generated through the analysis and integration of data including cores, borehole images (BHI), logs, mud losses, production logs, well test data and 3D seismic data. The impact of lithology on fracture occurrence was quantified based on rock-typing. Rock-types are distributed in a 3D geological model using a high resolution sequence stratigraphic framework. The length, dip angle and orientation of fractures, together with the shale content of the facies where they occur, were defined to sort the tectonic fractures from the non-tectonic (diagenetic) ones. It was found that multiple sets of tectonic diffuse fractures are generally associated with cleaner limestone units. Altogether, three sets of diffuse fractures were identified from BHI data: NE-SW, EW and NW-SE. In addition, large-scale fracture corridors, including sub-seismic faults identified from seismic analysis, were detected and calibrated with cores and BHI. The final model incorporates two scales of tectonic fractures with a direct bearing on field production behavior: diffuse fractures and large fracture corridors. Fracture calibration was performed using production logs and well production data. Permeability at wells was computed in the 3D fracture model and matched with real build-up data. These data were then used to propagate 3D fracture properties (fracture porosity, fracture permeability and equivalent block size) in the upscaled geological model, for constructing a full-field reservoir simulation model. Few changes of the fracture properties were needed to obtain a good history match, indicating that the fracture model produced is robust.

Multidomain Data and Modeling Unlock Unconventional Reservoir Challenges

Technology Update Completion strategy and hydraulic fracture stimulation are important keys to economic success in low- permeability and unconventional reservoirs such as tight sand and shale. Therefore, engineering workflows in unconventional reservoirs need to focus on completion and stimulation optimization, just as they do well placement and spacing. The primary obstacles associated with optimizing completions in these reservoirs have been the absence of hydraulic fracture models that properly simulate the complex fracture propagation common in many reservoirs, the lack of efficient methods to create discrete reservoir simulation grids to rigorously model hydrocarbon production from complex hydraulic fractures, the lack of automated fracture treatment staging algorithms, and the lack of ability to efficiently integrate microseismic mapping measurements with geological and geophysical data. This article details a novel approach for enabling efficient multistage completions, new complex fracture models, unstructured gridding-based reservoir simulation, and a comprehensive integrated workflow developed within single reservoir-centric stimulation design software. Though primarily focused on unconventional reservoirs, this efficient full-cycle seismic-to- simulation workflow is applicable also for conventional reservoirs. Seismic-to-Simulation Through Stimulation In recent years, an increasing amount of hydrocarbons in North America have been produced from unconventional reservoirs, with a similar trend expected worldwide. The International Energy Agency projects the hydrocarbon reserves in unconventional reservoirs to be as high as 8 trillion BOE. Most of these low-permeability reservoirs must be hydraulically fractured to produce. In unconventional reservoirs, stimulation is the key step in the seismic-to-simulation workflow. For unconventional reservoirs, detailed reservoir characterization plays a fundamental role in completion design, affecting the staging strategy, location of perforations or fracture ports, and stimulation design. Properly modeling the interaction of induced fractures with the rock fabric and coupling the hydraulic fracture geometry and conductivity with subsequent well performance is an essential element in this comprehensive workflow. The unconventional reservoir simulations include a detailed geologic description. The modeling is focused on the well and the specifics of the completion (i.e., the hydraulic fracture treatments). In contrast, this information can be significantly scaled up for conventional reservoirs, shifting the focus to large-scale reservoir behavior (i.e., multiwell and full-field simulation models).

The Potential of a Surfactant/Polymer Flood in a Middle Eastern Reservoir

An integrated full-field reservoir simulation study has been performed to determine the reservoir management and production strategies in a mature sandstone reservoir. The reservoir is a candidate for an enhanced oil recovery process or otherwise subject to abandonment. Based on its charateristics, the reservoir was found to be most suited for a surfactant/polymer (SP) flood. The study started with a large data gathering and the building of a full-field three-dimensional geological model. Subsequently, a full field simulation model was built and used to history match the water flood. The history match of the water flood emphasizes the areas with remaining high oil saturations, establishes the initial condition of the reservoir for an SP flood, and generates a forecast of reserves for continued water flood operations. A sector model was constructed from the full field model and then used to study different design parameters to maximize the project profitability from the SP flood. An economic model, based on the estimated recovery, residual oil in-place, oil price, and operating costs, has been implemented in order to optimize the project profitability. The study resulted in the selection of surfactant and polymer concentrations and slug size that yielded the best economic returns when applied in this reservoir. The study shows that, in today’s oil prices, surfactant/polymer flood when applied in this reservoir has increased the ultimate oil recovery and provide a significant financial returns.

Systematic Surveillance Techniques for a Large Miscible WAG Flood

Summary Miscible water-alternating-gas (WAG) flooding has proven to be an attractive enhanced-oil-recovery (EOR) method the world over. Successful WAG floods can yield significant additional oil recovery over waterflooding. WAG floods are complex in nature since reduction of residual oil in the pore spaces depends on mass transfer. Optimizing miscibile contact between the injected gas and the reservoir oil over a large rock volume is challenging. This challenge is more manageable in a small-scale pilot flood or a coreflood than in a large field implementation. Numerical-simulation efforts can provide guidance to designing an optimal flood. However, the field application will often reveal challenges that are not discovered in the pilot stage or by the full-field simulation model because the geologic properties and heterogeneity of the reservoir rock are not accurately represented. Integrated surveillance of a WAG flood is the only means to determine whether the flood is working efficiently and the planned additional recovery will be delivered. A well-implemented surveillance plan allows timely intervention to improve the efficiency of an underperforming WAG flood. This paper presents a systematic approach for applying EOR surveillance tools and methods in large miscible WAG floods in the Ivishak reservoirs at the Prudhoe Bay and Eileen West End (EWE) of the North Slope, Alaska. Highlights of these surveillance methods are (1) designed and implemented by a multidisciplinary team, (2) based on proven theory and corroborated with field data, (3) requires easily obtainable and relatively inexpensive field data and analysis, and (4) applied from fault block down to zone levels. Implementation of these tools has helped to identify the efficiency of flood patterns and areas of poor performance, which then can be modified through infill drilling, well recompletion, or WAG-ratio modification to maximize EOR recovery.

Innovative Alternative to Full-Field Compositional Modeling-Case Study of the North Kuwait Jurassic Complex

Summary The North Kuwait Jurassic complex (NKJC) consists of six fields with four potential reservoirs in a naturally fractured Jurassic carbonate formation. Current understanding of the complex has led to 12 subdivisions of the area and potentially 48 separate compartments (segments) in the complex. These subdivisions are defined by fault boundaries supported by a combination of variations in fluid composition, initial pressures, and free-water levels estimated from capillary pressure and log saturation data. Multiscenario production forecasts based on integrated full-field modeling were needed in the process of building a field-development plan (FDP) for the NKJC. An integrated asset modeling (IAM) framework is adopted in which multiple separate reservoir models are coupled through global constraints to meet gas-delivery targets. We present the results of a feasibility study to select the optimal modeling strategy for the complex. We discuss available options to simulate multiple reservoirs, which, although isolated based on the current understanding of compartmentalization, need to meet global production targets. We show that using multiple-reservoirs integration through a controller to couple the separate reservoir models through global production targets/limits provides an optimal simulation framework for the NKJC—the flexibility and computational efficiency of multiple segment models and the comprehensiveness of full-field simulation modeling. The solution uses a black-oil delumping technique to obtain compositional wellstreams while running black-oil simulation models. The feasibility study demonstrated that black-oil delumping enables the composition and component molar rates of a producing well from a black-oil reservoir simulation to be reconstituted accurately. Comparison of results obtained from a compositional model to those obtained from a black-oil model using black-oil delumping shows excellent agreement. The adopted simulation framework provided us with all the benefits of a compositional full-field simulation model while adding two advantages (i.e., computational speed and flexibility). Full computational advantages of black-oil modeling are obtained while preserving the reservoir details required for an accurate prediction of well and reservoir behavior.