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Abstract Chemical flooding has been widely used to enhance oil recovery after conventional waterflooding. However, it is always a challenge to model chemical flooding accurately since many of the model parameters of the chemical flooding cannot be measured accurately in the lab and even some parameters cannot be obtained from the lab. Recently, the ensemble-based assisted history matching techniques have been proven to be efficient and effective in simultaneously estimating multiple model parameters. Therefore, this study validates the effectiveness of the ensemble-based method in estimating model parameters for chemical flooding simulation, and the half-iteration EnKF (HIEnKF) method has been employed to conduct the assisted history matching. In this work, five surfactantpolymer (SP) coreflooding experiments have been first conducted, and the corresponding core scale simulation models have been built to simulate the coreflooding experiments. Then the HIEnKF method has been applied to calibrate the core scale simulation models by assimilating the observed data including cumulative oil production and pressure drop from the corresponding coreflooding experiments. The HIEnKF method has been successively applied to simultaneously estimate multiple model parameters, including porosity and permeability fields, relative permeabilities, polymer viscosity curve, polymer adsorption curve, surfactant interfacial tension (IFT) curve and miscibility function curve, for the SP flooding simulation model. There exists a good agreement between the updated simulation results and observation data, indicating that the updated model parameters are appropriate to characterize the properties of the corresponding porous media and the fluid flow properties in it. At the same time, the effectiveness of the ensemble-based assisted history matching method in chemical enhanced oil recovery (EOR) simulation has been validated. Based on the validated simulation model, numerical simulation tests have been conducted to investigate the influence of injection schemes and operating parameters of SP flooding on the ultimate oil recovery performance. It has been found that the polymer concentration, surfactant concentration and slug size of SP flooding have a significant impact on oil recovery, and these parameters need to be optimized to achieve the maximum economic benefit.
Summary Chemical–enhanced oil–recovery (CEOR) mechanisms are strongly influenced by gridblock size and reservoir heterogeneity compared with conventional waterflooding (WF) simulations. In WF–simulation models, simulation grids are commonly upscaled (coarsened) on the basis of a single–phase flow to perform history matching and sensitivity analyses within affordable computational times. However, this coarse–grid resolution (typically, approximately 100 ft) is insufficient for CEOR, and hence usually fails to capture key physical mechanisms. These coarse models also tend to increase numerical dispersion, artificially increase the level of mixing, and have inadequate resolution to capture certain geological features to which EOR processes can be highly sensitive. Thus, coarse models often overestimate the sweep efficiency as a result of numerical dispersion, and underestimate the displacement efficiency as a result of the artificial dilution of chemicals. Therefore, grid refinement is necessary for CEOR simulations when the original (fine) Earth model is not available or when major disconnects occur between the original Earth model and the history–matched coarse WF model. However, recreating the fine–scale heterogeneity without degrading the history match from the coarse grid remains a challenge. Because of the different recovery mechanisms involved in CEOR, such as miscibility and thermodynamic phase behavior, the impact of grid downscaling on CEOR simulations is not well–understood. In this work, we introduce a geostatistical downscaling method that can be conditioned to tracer data, for refining coarse history–matched WF models. The proposed downscaling method refines the coarse grid and populates the relevant properties in the newly created, finer gridblocks, reproducing the fine–scale heterogeneity while retaining the fluid material balance. The method treats the values of rock properties in the coarse grid as hard data, and the corresponding variograms and property distributions as soft data. We outline a work flow that reduces uncertainties in the geological properties by integrating dynamic data such as sweep efficiency from the interwell tracers. We provide several test cases, and demonstrate the applicability of the proposed method to improving the history match of a CEOR pilot.
Olalotiti-Lawal, Feyi (Texas A&M University) | Onishi, Tsubasa (Texas A&M University) | Kim, Hyunmin (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University) | Fujita, Yusuke (JX Nippon Oil and Gas Exploration Corporation) | Hagiwara, Kenji (JX Nippon Oil and Gas Exploration Corporation)
Summary We present a simulation study of a mature reservoir for carbon dioxide (CO2) enhanced-oil-recovery (EOR) development. This project is currently recognized as the world's largest project using post-combustion CO2 from power-generation flue gases. With a fluvial formation geology and sharp hydraulic-conductivity contrasts, this is a challenging and novel application of CO2 EOR. The objective of this study is to obtain a reliable predictive reservoir model by integrating multidecadal production data at different temporal resolutions into the available geologic model. This will be useful for understanding flow units along with heterogeneity features and their effect on subsurface flow mechanisms, to guide the optimization of the injection scheme and maximize CO2 sweep and oil recovery from the reservoir. Our strategy consists of a hierarchical approach for geologic-model calibration incorporating available pressure and multiphase production data. The model calibration is performed using regional multipliers, and the regions are defined using a novel adjacency-based transform accounting for the underlying geologic heterogeneity. The genetic algorithm (GA) is first used to match 70-year pressure and cumulative production by adjusting pore volume (PV) and aquifer strength. Water-injection data for reservoir pressurization before CO2 injection is then integrated into the model to calibrate the formation permeability. The fine-scale permeability distribution consisting of more than 7 million cells is reparameterized using a set of linear-basis functions defined by a spectral decomposition of the grid-connectivity matrix (Laplacian grid). The parameterization represents the permeability distribution using a few basis-function coefficients that are then updated during history matching. This leads to an efficient and robust work flow for field-scale history matching. The history-matched model provided important information about reservoir volumes, flow zones, and aquifer support that led to additional insight compared with previous geological and simulation studies. The history-matched field-scale model is used to define and initialize a detailed fine-scale model for a CO2 pilot area that will be used to study the effect of fine-scale heterogeneity on CO2 sweep and oil recovery. The uniqueness of this work is the application of a novel geologic-model parameterization and history-matching work flow for modeling of a mature oil field with decades of production history, and which is currently being developed with CO2 EOR.
Olalotiti-Lawal, Feyi (Texas A&M University) | Onishi, Tsubasa (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University) | Fujita, Yusuke (JX Nippon Oil & Gas Exploration Corporation) | Watanabe, Daiki (JX Nippon Oil & Gas Exploration Corporation) | Hagiwara, Kenji (JX Nippon Oil & Gas Exploration Corporation)
Abstract We present a detailed history matching and process optimization of a CO2 WAG pilot at the West Ranch Field, Texas. The Petra Nova, a 50/50 joint venture between NRG and JX Nippon, operates a commercial scale post-combustion carbon capture facility at NRG's WA Parish generating station southwest of Houston. The industrially sourced CO2 is utilized for EOR in the West Ranch field, which is operated by Hilcorp Energy. A CO2 pilot was conducted to examine the potential for tertiary oil recovery by pattern-scale CO2 flooding and to better understand how pattern flooding will work in the fluvial geology of the reservoir. This paper discusses detailed modeling and history matching of the CO2 EOR pilot at the West Ranch field to understand the CO2 plume movement and optimize the flood design parameters. For history matching the CO2 WAG pilot, we started by initializing the pilot sector model and imposing multiphase boundary fluxes generated from full-field compositional simulation. The pilot model calibration followed a hierarchical two-step approach. First, we performed a large-scale update of the model permeability distribution by integrating bottomhole pressure and multiphase production data. Next, streamline-based local updates were used to further calibrate the permeability field to match CO2 breakthrough times at the producers. Sensitivity studies were conducted using the calibrated pilot model to evaluate the effects of WAG ratio, CO2 Pore Volume Injected (PVI), number of WAG cycles and Voidage Replacement Ratio (VRR) on the oil recovery efficiency and CO2 utilization factor. Finally, we carried out a multiobjective optimization of the CO2 WAG process based on the influential operational parameters. With this strategy, multiple scenarios that consider the trade-off between the oil recovery efficiency and CO2 utilization factor were generated. The history matched models capture the major trends observed in the producing Gas-Oil Ratio (GOR), oil production rate and CO2 mole fraction history at the wells. The updated models were independently validated in two ways. First, the models showed good agreement with reservoir saturation logs at two observation wells. Second, the models reproduced the CO2 recovery as a fraction of the injected CO2. These results imply that the history matched models are able to adequately capture the CO2 sweep profile in the pilot area. Sensitivity studies indicate CO2PVI and VRR (Voidage Replacement Ratio) as dominant parameters impacting oil recovery efficiency. The VRR and WAG ratio play a significant role in determining the CO2 utilization factor. A set of optimal operational parameters that utilize these decision variables was generated using a Design of Experiments (DOE) based multiobjective optimization workflow. This work presents, for the first time, modeling and optimization of a field-scale post-combustion CO2 WAG process.
Olalotiti-Lawal, Feyi (Texas A&M University) | Onishi, Tsubasa (Texas A&M University) | Kim, Hyunmin (Texas A&M University) | Datta-Gupta, Akhil (Texas A&M University) | Fujita, Yusuke (JX Nippon Oil & Gas Exploration Corporation) | Hagiwara, Kenji (JX Nippon Oil & Gas Exploration Corporation)
Abstract We present a simulation study of a mature reservoir for CO2Enhanced Oil Recovery (EOR)development. This project is currently recognized as the world's largest project utilizing post-combustion CO2 from power generation flue gases. With a fluvial formation geology and sharp hydraulic conductivity contrasts, this is a challenging and novel application of CO2 EOR. The objective of this study is to obtain a reliable predictive reservoir model by integrating multi-decadal production data at different temporal resolutions into the available geologic model. This will be useful for understandingflow units, heterogeneity features and their impact on subsurface flow mechanisms to guide the optimization of the injection scheme and maximize CO2 sweep and oil recovery from the reservoir. Our strategy consists of a hierarchical approach for geologic model calibration incorporating available pressure and multiphase production data. The model calibration is carried out using regional multipliers whereby the regions are defined using a novel Adjacency Based Transform (ABT) accounting for the underlying geologic heterogeneity. To start with, the Genetic Algorithm (GA) is used to match 70-year pressure and cumulative production by adjusting pore volume and aquifer strength. Water injection data for reservoir pressurization prior to CO2 injection is then integrated into the model to calibrate the formation permeability. The fine-scale permeability distribution consisting of over 7 million cells is reparametrized using a set of linear basis functions defined by a spectral decomposition of the grid connectivity matrix (grid Laplacian). The parameterizationrepresents the permeability distributionusing a few basis function coefficients which are then updated during history matching. This leads to an efficient and robust workflow for field scale history matching. The history matched model provided important information about reservoir volumes, flow zones and aquifer support that led to additional insight to the prior geological and simulation studies. The history matched field-scale model is used to define and initialize a detailed fine-scale model for a CO2 pilot area which will be utilized for studying the impact of fine-scale heterogeneity on CO2 sweepand oil recovery. The uniqueness of this work is the application of a novel geologic model parameterization and history matching workflow formodeling of a mature oil field with decades of production history and which is currently being developed with CO2 EOR.
Ampomah, W.. (Petroleum Recovery Research Center) | Balch, R. S. (Petroleum Recovery Research Center) | Cather, M.. (Petroleum Recovery Research Center) | Rose-Coss, D.. (Petroleum Recovery Research Center) | Gragg, E.. (Petroleum Recovery Research Center)
Abstract This paper presents a numerical study of CO2 enhanced oil recovery (EOR) and storage in partially depleted reservoirs. A field-scale compositional reservoir flow model was developed for assessing the performance history of a CO2 flood and optimizing oil production and CO2 storage in the Farnsworth Field Unit (FWU), Ochiltree County, Texas. A geocellular model was constructed from geophysical and geological data acquired at the site. The model aided in characterization of heterogeneities in the Pennsylvanian-aged Morrow sandstone reservoir. Seismic attributes illuminated previously unknown faults and structural elements within the field. A laboratory fluid analysis was tuned to an equation of state and subsequently used to predict the thermodynamic minimum miscible pressure (MMP). Datasets including net-to-gRose ratio, volume of shale, permeability, and burial history were used to model initial fault transmissibility based on the Sperivick model. An improved history match of primary and secondary recovery was performed to set the basis for a CO2 flood study. The performance of the current CO2 miscible flood patterns were subsequently calibrated to historical production and injection data. Several prediction models were constructed to study the effect of recycling, addition of wells and/or new patterns, water alternating gas (WAG) cycles and optimum amount of CO2 purchase on incremental oil production and CO2 storage in the FWU. The history matching study successfully validated the presence of the previously-undetected faults within FWU that were seen in the seismic survey. The analysis of the various prediction scenarios showed that recycling a high percentage of produced gas, addition of new wells and a gradual reduction in CO2 purchase after several years of operation would be the best approach to ensure a high percentage of recoverable incremental oil and sequestration of anthropogenic CO2 within the Morrow reservoir.
Summary Conventional miscible or near-miscible gasflooding simulation often overestimates oil recovery, mostly because it does not capture a series of physical effects tending to limit interphase compositional exchanges. Those can include microscopic bypassing of oil situated in dead-end pores or blocked by water films, as well as macroscopic bypassing caused by subgrid-size heterogeneities or fingering. We here present a new engineering solution to this problem in the near-miscible case, relying on our in-house research reservoir simulator. The principle is, while using a black-oil or an equation-of-state description, to dynamically decrease the K-value of heavy components and possibly increase the K-value of light components as the oil saturation reaches the desired residual limit; this enables changing the phase boundaries when needed while preserving the original fluid behavior during the initial production stages. The benefits of the proposed solution are demonstrated on a reservoir-conditions tertiary-gas-injection experiment, performed in our laboratories, for which residual saturations as well as oil-phase and individual-component production rates have easily and successfully been history matched. Results are then compared with matches obtained by use of saturation exclusion and α-factors methods. As a proof of concept, the suitability of the new method to simulate incomplete revaporization of condensate during gas cycling is also illustrated, on the third SPE comparative-solution-project case.
Abstract In a matured flood, reservoir surveillance is critical for optimizing injection and increasing/maintaining production. Reservoir surveillance aims at identifying wells that show symptoms of mechanical and/or reservoir issues and a possible diagnosis. From a reservoir standpoint, three issues that are commonly encountered while managing injection-driven recovery processes are quantifying well connectivity, identifying conformance problems, and locating bypassed oil. These issues are exaggerated in a gas injection project. In this paper, a methodology is presented to develop a tool that incorporates both geological and production data to analyze field surveillance data. The focus of the paper is to demonstrate the use of streamlines to optimize injection/production. A heterogeneous limestone reservoir in the US Permian Basin was used as a case study. This field has between 100-150 active wells and has been on continuous CO2 injection for nearly three decades. Hence understanding injector-producer connectivity becomes critical for pattern balancing and identifying gas cycling between injectors and producers. The objective of the study was to develop an easy-to-use visual tool that aids analysis of surveillance data to: Seek opportunities for improving oil recovery through better CO2 allocation. Identify infill-drilling opportunities by locating unswept areas. Full-field compositional simulation modeling was done in E300 to obtain a reasonable history-matched model. The geology of the field is fairly complicated from the streamline generation point of view because of the unconformities and faults present in the reservoir. Streamlines were generated using a program called DESTINY. These streamlines were then imported to Petrel-RE for visualization. Dynamic allocation factors were calculated based on flux distribution. These streamlines were superimposed on other static and dynamic reservoir property maps. Additional areas that required increase in injection support as well as new infill locations were identified using this workflow. In addition, candidates for conformance jobs were identified that could potentially increase daily field production.
Abstract Fluids injected during secondary and tertiary floods often leave parts of the reservoir unswept mostly because of large heterogeneity and mobility ratio. Several applications require an analytical scheme that could predict production with as few parameters possible. We develop such an analytical model of volumetric sweep that aims to apply an extension of Koval's theory (Jain and Lake, 2013) where flow is assumed to be segregated under vertical equilibrium conditions for secondary and tertiary displacements. The original Koval factor is applicable for upscaling secondary miscible floods. The new analytical model for secondary and tertiary floods is applied to provide quick estimates of oil recovery of miscible as well as immiscible displacements, which is then calibrated against field data. The model parameters: Koval factor, sweep efficiency and pore volume, estimated after history matching could be used to make reservoir management decisions. The model is very simple; history matching can be done in a spreadsheet. Single-front, gravity-free, displacements can be modeled using Koval factors. Two-front, gravity-free, displacements can also be modeled using Koval-type factors for both the fronts. These Koval-type factors, coupled with laboratory scale relative permeabilities, allows for scaling the displacement to a larger reservoir system. The new method incorporates by-passed pore volume as a parameter, a difference between this work and that of Molleai (2012), along with Koval factors and local front velocities. For two front displacements, it also accounts for the interaction between the fronts which honors correct mass conservation, another difference with the work of Molleai (2012). The results from new models for secondary and tertiary displacements were verified by comparing them against numerical simulations. The application is also demonstrated on actual field examples. Current techniques for reservoir surveillance rely on numerical models. The parameters on which these numerical models depend on are very large in number, introducing large uncertainty. This technique provides a way to predict performance without the use of computationally expensive fine scale simulation models, which could be used for reservoir management while reducing the uncertainty.
Abstract This study deals with simulation model of Foam Assisted Water Alternating Gas (FAWAG) method that had been implemented to two Norwegian Reservoirs. Being studied on number of pilot projects, the method proved successful, but Field Scale simulation was never understood properly. New phenomenological foam model was tested with sensitivity analysis on foam properties to provide a guideline for the history matching process (GOR alteration) of FAWAG Pilot of Snorre Field (Statoil). The aim was to check the authenticity of presented new foam model in commercial software whether it is implementable on a complex geological model for quick feasibility studies, either for onward practical pilot or as justification for more detailed technical study. The simulation showed that Foam model is applicable. The mismatch between history and actual GOR in some periods of injection is due to the complexity of the fluid flows control inside reservoir. The way; how specific properties control the time of gas arrival and values of GOR are described. The analyses of the improvements in the injection schedule are shown. With increasing number of CO2 and FAWAG methods in preparation worldwide, the use of the simulation contributes to more precise planning of the schedule of water and gas injection, prediction of the injection results and evaluation of the method efficiency. The testing of the surfactant properties allows making grounded choice of surfactant to use. The analysis of the history match gives insight in the physics of in-situ processes. Detailed Qualitative analysis is presented for foam modeling against the FAWAG historical data that provides sharp idea of the behavior of Foam model for different foam factors, which in turn provides reasons for the unpredictable foam behavior in WFB Project and also serve as quick reference for future general foam pilot simulations at field scale.