Sequential Quadratic Programming for Solving Constrained Production Optimization–Case Study From Brugge Field

Comparison of SQP and AL algorithms for deterministic constrained production optimization of hydrocarbon reservoirs

Smart completion allows higher operational flexibility for the development of petroleum fields than the conventional completion. However, the real benefits of this technology are not always clear. In order to compare advantages and disadvantages of each type of completion in the exploitation of oil fields, it is necessary to optimize the production strategy for both options. This paper presents a methodology to optimize strategies with reactive and proactive control valves in smart wells; the same methodology is applied to conventional wells in order to compare the different behaviors. This methodology aims to help the manager in the decision of choosing between conventional or smart wells in developing an oil field. An evolutionary algorithm was coupled to a commercial simulator to search for the maximum of the objective function, the Net Present Value (NPV), determining the optimum water cut of producer smart wells for each valve (proactive control), for all valves (reactive control) or for a whole producer conventional well. Then, a fair performance comparison between both types of completions is done. The case studies are simple in order to make clear the difference between the cases. They are classified in reservoir models regarding different heterogeneities, type of oil and under economic uncertainty. Uncertainties in oil prices and in water production cost are considered through three economic scenarios: optimistic, probable and pessimistic. Results show that smart wells are able to increase production time, cumulative oil production and the NPV, also decreasing water production and injection in some cases. The results show higher benefits in using smart wells in high heterogeneity and light oil reservoirs to increase oil production and maximize NPV. Smart wells differ greatly from the conventional ones in pessimistic economic scenarios, where there are operational restrictions due to the unfavorable scenario. Introduction Nowadays, smart wells (SW) can be considered to improve oil recovery in petroleum fields, justifying the necessity of studies to verify the applicability of this type of wells. In SW, completion has parkers that allow partitioning of the wellbore, downhole inflow control valves (ICV) and a variety of sensors, especially those that monitor flow, pressure and temperature, installed on the production tubing. The smart completion allows greater operational flexibility in developing the field, enabling them to take action over time of production, capable of responding to (or preventing) undesired events, avoiding a costly intervention in the well during production. However, many companies today still do not feel fully confident in investing in a more expensive technology, because the real benefits of this technology are still not clear. Part of this lack of confidence is because there is no consolidated methodology that shows the advantages and disadvantages of SW in relation to conventional wells (CW).

SummaryIn this work, we investigate the efficient estimation of the optimal design variables that maximize net present value (NPV) for the life-cycle production optimization during a single-well carbon dioxide (CO2) huff-n-puff (HnP) process in unconventional oil reservoirs. A synthetic unconventional reservoir model based on Bakken Formation oil composition is used. The model accounts for the natural fracture and geomechanical effects. Both the deterministic (based on a single reservoir model) and robust (based on an ensemble of reservoir models) production optimization strategies are considered. The injection rate of CO2, the production bottomhole pressure (BHP), the duration of injection and the production periods in each cycle of the HnP process, and the cycle lengths for a predetermined life-cycle time can be included in the set of optimum design (or well control) variables. During optimization, the NPV is calculated by a machine learning (ML) proxy model trained to accurately approximate the NPV that would be calculated from a reservoir simulator run. Similar to the ML algorithms, we use both least-squares (LS) support vector regression (SVR) and Gaussian process regression (GPR). Given a set of forward simulation runs with a commercial compositional simulator that simulates the miscible CO2 HnP process, a proxy is built based on the ML method chosen. Having the proxy model, we use it in an iterative-sampling-refinement optimization algorithm directly to optimize the design variables. As an optimization tool, the sequential quadratic programming (SQP) method is used inside this iterative-sampling-refinement optimization algorithm. Computational efficiencies of the ML proxy-based optimization methods are compared with those of the conventional stochastic simplex approximate gradient (StoSAG)-based methods. Our results show that the LS-SVR- and GPR-based proxy models are accurate and useful in approximating NPV in the optimization of the CO2 HnP process. The results also indicate that both the GPR and LS-SVR methods exhibit very similar convergence rates, but GPR requires 10 times more computational time than LS-SVR. However, GPR provides flexibility over LS-SVR to access uncertainty in our NPV predictions because it considers the covariance information of the GPR model. Both ML-based methods prove to be quite efficient in production optimization, saving significant computational times (at least 4 times more efficient) over a stochastic gradient computed from a high-fidelity compositional simulator directly in a gradient ascent algorithm. To our knowledge, this is the first study presenting a comprehensive review and comparison of two different ML-proxy-based optimization methods with traditional StoSAG-based optimization methods for the production optimization problem of a miscible CO2 HnP.

Due to large dimensionality of typical field optimization problems (in terms of either the number of the optimization variables or the number of equations that needs to be solved at every simulation time-step), most approaches for field optimization are local iterative methods. Due to their local nature, achievable optima by these methods are directly impacted by the choice of the initial guess for the sought optimal profiles. Here we propose a simple, low-cost framework to efficiently initialize localsearch optimization algorithms, with the focus on waterflooding optimization under a standard net-present value (NPV) objective. The procedure relies on accepted reservoir engineering concepts and consists of two sequential steps. First, a common economical 'shut-in' water-cut (WCT) is determined by rewriting the (assumed) positive NPV contribution of the individual completion in terms of WCT. If the WCT reached during the production from a given completion is lower than the economical 'shut-in' WCT the objective function is insensitive to that particular control variable. An adjusted shut-in WCT is therefore defined for each completion as the minimum of the economic WCT and the 'reached' WCT. Then, using these shut-in WCT values in a (rather limited) number of reservoir simulation runs, coefficients in a group production guide rate formula for the preferred phase are determined yielding a maximized NPV. Such a guide rate formula is normally implemented in all state of the art reservoir simulators. The guide rate coefficients and the economic and adjusted WCT values are used to initialize the WCT optimization problem. The initialization strategy was applied to history matched models of the Brugge field [1]. The optimization algorithms were Hooke-Jeeves (HJ), Nelder-Mead and Sequential Quadratic Programming. With any of these methods, the optimized shut-in WCTs in terms of the achieved NPV clearly outperformed the best NPV solution obtained so far on these history matched models (HJ being the best choice). The combined economic and adjusted WCT gave additional improvement in NPV. The injecting strategy was chosen to be the unity voidage replacement ratio, which decreased the number of variables considerably. Introduction Finding an optimal depletion strategy for hydrocarbon production has naturally always been a key subject in reservoir management, the underlying problem to be solved generally being maximization of a key quantity as oil production, net present value (NPV), etc. In the past, optimal settings of the optimization parameters were almost exclusively determined in a manual fashion, which generally is a quite time consuming procedure with a high likelihood of obtaining suboptimal results. While manual approaches are still predominant strategies in the reservoir management practice, due to maturity of most existing major oilfields and a gradual decrease in large oil discoveries, research for more systematic optimization approaches has been initiated. Not surprisingly, most 'advances' have so far been made in systematic optimization, at least in theory, of waterflooding recovery processes, see e.g. Yeten et al. (2003); Brouwer and Jansen (2004); Sarma et al. (2005); Lorentzen et al. (2006); Wang et al. (2007); Chen et al. (2008); Sarma et al. (2008), Sarma and Chen (2008) [2–9]. The spectrum of the proposed (waterflooding) optimization techniques is already quite broad. Most of the proposed methods are local-search iterative methods, ranging from basic steepest descent iterative updates of the control variables involving either deterministic or stochastic objectivefunction gradient/sensitivity approximations, e.g. Wang et al. (2007); Chen et al. (2008) [6–7] to 'direct search' methods, as e.g. Nelder-Mead or Hooke-Jeeves [10–11], not requiring any derivative information.

Background: In high-pressure carbonate reservoirs, miscible gas injection is a key method for enhancing oil recovery and maintaining reservoir pressure. The main challenge lies in controlling bottomhole pressure (BHP) and tubing head pressure (THP) while maximizing injection volumes and preventing formation fracturing. Current strategies typically rely on THP regulation and injection rates as the primary means of well control. Aim: This study aims to analyze an optimized gas injection strategy based on precise well control to prevent formation fracturing and improve injection efficiency. Materials and methods: The study uses production and geological data analysis, empirical forecasting models, and statistical techniques to enhance the accuracy and reliability of predictions. Modern algorithms and data-processing technologies are applied to handle large datasets, allowing for more accurate and consistent forecasts of key field development indicators. Results: The results indicate that gas injection can be optimized by lowering reservoir pressure and increasing tubing head pressure (THP). This stabilizes bottomhole pressure (BHP) because of increased frictional losses in the tubing string. Controlling THP and gas rates allows stable BHP operation. Currently, maximum BHP limits have been established for the wells, while allowable THP is restricted but can be increased based on previous test results. An increase in THP would enable higher gas injection volumes, leading to improved oil recovery. BHP remained within safe limits and was monitored directly with downhole pressure gauges. Conclusion: This study presents an optimized approach to gas injection management, based on real-time pressure monitoring, well–reservoir nodal analysis, dynamic control of tubing head pressure (THP), and regulation of gas flow rates. The results emphasize the need to consider nonlinear pressure losses when designing safe and efficient injection strategies. Considering these effects helps prevent formation fracturing and ensures long-term reservoir integrity.

When new wells are planned, typically the same trajectory is used for assessing the effectiveness of conventional wells and wells with smart completions. This study demonstrates that the economically optimized trajectory for smart and conventional wells can be very different. Two new well trajectory optimization algorithms were developed using Stochastic Pattern Search (SPS) principles. In both algorithms, random perturbations are made starting from an initial well trajectory, which are sent to a reservoir simulator. Thereafter the perturbation with the highest Net Present Value (NPV) is selected. New perturbations of the selected well trajectory are made and simulated to, again, select the highest NPV. This process is repeated until a certain stopping criterion is met. The two methods differ in the selection of the perturbations used to initiate the new iteration and have a slightly different computational performance. To demonstrate the difference between the optimal well trajectory of a well with a conventional and a smart completion, both the SPS1 and SPS2 method were evaluated using a realistic, but slightly simplified reservoir model. Both methods were able to optimize the trajectory for both conventional and smart completions. The SPS1 method quickly converged to a local optimum, whilst the SPS2 method was able to determine a trajectory with a significantly higher NPV for both the conventional and smart wells. Moreover, the optimal well trajectory with the smart completion, as found by the SPS2 algorithm, had an NPV that was 40% higher than the smart well with the trajectory which was optimal for the conventional completion. Following the above, it can be concluded that when smart completions are assessed, well trajectory (re)optimization can have a very significant value impact and may be crucial in evaluating the full potential of the completion. Furthermore, it was shown that, for the investigated case, the SPS2 procedure is a good method for well trajectory optimization in a three-dimensional reservoir and, although more testing is needed, it is believed that it has potential to work with any type of completion.

Investments in oil and gas projects are driven by critical field development decisions including well placement, which often significantly affect the projects’ economics. Due to their typically high cost and inherently insufficient data (especially in greenfields, or fields in their early stage of development), managing uncertainty is critical when optimizing a field development plan. The use of a single deterministic base case for hydrocarbon, both in place assessment and production forecasting is often misleading and leads to sub-optimal decisions. Consequently, robust field development plans require multiple geological realizations covering the range of uncertainty in reservoir properties and encompassing both multiple geological concepts and geostatistical properties distribution. Typically, an objective function such as the average net present value (NPV) or the average cumulative oil production (COP) is optimized in order to select an optimal development scenario. Nevertheless, such an assessment can be computationally prohibitive, especially when using optimization methods require hundreds, often thousands of costly simulations over a single realization, a number that significantly increases when multiple realizations are involved. This study proposes a new method for well placement optimization under uncertainty, building on map-based evolutionary optimization technique: the black hole particle swarm optimization (BHPSO). The statistical net hydrocarbon thickness (SNHCT) map is introduced to guide the BHPSO algorithm; and hence, pragmatically account for uncertainty in the process of well placement optimization. We optimize well placement on the realization corresponding to the minimum difference between its NHCT map and the SNHCT map. The SNHCT combines the average and the P90 NHCT maps; hence, assuring that the selected sweet spots for well placement are statistically the best with regard to the multiple subsurface realizations. The method is applied on the Olympus benchmark case and results are compared to two scenario reduction methods: RMfinder and k-means-k-medoids Clustering. Results show superior performance over the two methods in terms of optimality of the result and the required computational load.

An onshore oil well in Asia with depleting reservoir pressure and severe liquid loading was identified to be energized for continued production. The solution was needed to furnish the dual purpose of providing gas injection for liquid unloading with the additional feature of a surface controlled dual flow safety valve to isolate both the injection and production conduits. The well was completed with 4-1/2 × 3-1/2 × 2-3/8 in. tubing design producing from 2 reservoir layers and, anticipated an early water cut. The conventional solution is to re-complete the well with GLV. Weatherford proposed a retrofit in-tubing gas lift system with minimal surface modifications and no workover. With the In-tubing gas lift system, the installation could be performed on a Coiled Tubing unit comprising of a smaller crew and reduced Carbon emission compared to a Workover rig. Furthermore, the system does not rely on annular integrity and enables gas injection below the production packer depth. Both injection and production are performed within the tubing bore itself. A comprehensive study of Nodal analysis using simulation software was performed to assess system performance, identify coiled tubing size, and gas injection depth. It was inferred that a gas injection depth of 2800 m would change well deliverability performance and increase oil production. Sensitivity studies on water cut were performed for 10%, 15%, 20%, and 95%, and a gas injection rate of 500 to 2000 MMSCFPD. Optimum gas injection rates were found to be 500 to 1000 MMSCFPD, with 1.25 inch OD coiled tubing exhibiting maximum flow area and minimal pressure drop due to frictional losses. For a high water cut scenario of 95%, a high gas injection rate of 2000 MMSCFPD was anticipated from the simulation. Bottom injection string was run and installed using Hanger and Dual Flow Safety Valve. This safety valve landed across the locked open and communicated existing subsurface safety valve. The top section was run and latched into the polished bore, while also landing the top hanger into a modified spool adaptor on the surface. With this system in the well, coiled tubing acted as the gas injection path and the coiled tubing/tubing annulus provided the production path. Control line pressure release closed the flapper in the dual flow safety valve, stopping fluid movement in both the injection and production flow areas. The simulations and sensitivities performed utilizing the actual well test data enabled the operator to choose the optimum operating parameters and coiled tubing string size to be utilized in the system, and provided the option of deepening the gas injection point to a depth even below the pre-existing side pocket mandrels. The remedial completion system saved the operator from workover costs through a rigless operation with minimal personnel and a reduced carbon footprint. This paper details the implementation of an in-tubing gas lift system in the well facing liquid loading challenges covering the various stages of the project from simulation and analysis of the candidate well to the implementation of the system and the post-installation production outcome. The following aspects of the Inverse Gas Lift system installation will be discussed in this paper: Well Background and Data CollectionSimulation and AnalysisSystem OverviewOperations & System InstallationResults and Conclusion

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200316, “Joint Optimization of Well Completions and Controls for CO2 Enhanced Oil Recovery and Storage,” by Bailian Chen, SPE, and Rajesh Pawar, Los Alamos National Laboratory, prepared for the 2020 SPE Improved Oil Recovery Conference, originally scheduled to be held in Tulsa 18–22 April. The paper has not been peer reviewed. Carbon dioxide (CO2) storage through CO2 enhanced oil recovery (EOR) has been considered an option for larger-scale deployment of CO2 storage because of the economic benefits of oil recovery, 45Q tax credits, and the use of existing infrastructure. The complete paper investigates how optimal reservoir management and operation strategies can be used to optimize both CO2 storage and oil recovery. Results of the authors’ study showed that joint optimization of well completions and well controls can achieve a higher final net present value (NPV) than that obtained from the optimization of well controls only. Introduction In CO2 EOR associated with storage processes, poorly designed well-operating conditions or completions can lead to low oil recovery factors and suboptimal CO2 storage. Co-optimization of oil production and CO2 storage has been recognized as a feasible technique to maximize benefit in terms of oil production and CO2 storage tax credit. To the best of the authors’ knowledge, settings for well completions have not been considered as optimization variables in a CO2 EOR and storage co-optimization process. The objective of this study is to conduct joint optimization of well completions and controls [well rates or bottomhole pressures (BHP)] that maximize life-cycle NPV in CO2 EOR and storage processes and demonstrate the superiority of joint optimization over well-control-only optimization. Optimization Problem In this study, the optimization problem considered is the joint optimization of well completions and well controls for a CO2 EOR and storage process. The mathematical process behind this determination is detailed in the complete paper. The optimization problem was focused on jointly estimating the well completions (i.e., fraction of injection/production well perforations in each reservoir layer) and CO2 injection and oil-production controls that maximize NPV in a CO2 EOR and storage operation. The authors used a newly developed stochastic simplex approximate gradient algorithm to solve the optimization problem. The performance of the joint optimization approach was compared with the performance of the well-control-only optimization approach. In addition, the performance of the co-optimization of CO2 storage and oil-recovery approach was compared with that of the maximization of only-CO2-storage and only-oil-recovery approaches.

There are many mature oil fields in the North Sea. One of them is the Clair Oil Field, located on the United Kingdom Continental Shelf (UKCS) in Scotland. This oil field is unique because of the results of failure studies of several fluid injections, constituting secondary and tertiary oil recovery techniques, on increasing oil production. Only waterflooding has shown a promising future. In this paper, three-dimensional (3D) numerical simulations investigate the influence of infill drilling optimization by waterflooding in the Clair Oil Field so as to maximize net present value (NPV). An important goal of this study of reservoir management was to determine optimal well locations and optimal operational parameters (production constraints) that maximize cumulative oil production. Theoretical ideas and mathematical models with field data performance demonstrated the factors that can be considered in infill drilling so as to increase oil reserves. Most of the factors were related to reservoir heterogeneities and were important quantitatively for fluid injection and production. The results revealed that the four infill wells added in this study led to an increase in the field’s total cumulative oil production from 7.91×106 to 9.39×106 m3. The NPV after infill drilling optimization by waterflooding increased by 54.4% over the base case. The oil recovery factor rose from 17.2% to 20.2%, while the gas oil ratio (GOR) fell from 1,000 to 840 SCF/STB. After combining infill drilling with waterflooding, the horizontal infill producers outperformed vertical infill producers by 5.5%. The field water cut was reduced from 95% to 88%. In addition, an analysis of cumulative production between parent and child wells was made. It was found that child wells produce less cumulative oil production than do parent wells. This paper reveals the greater success of a combination of infill drilling with waterflooding in enhancing oil production compared to other findings where the influence of infill drilling and waterflooding were analyzed separately or compared. It can be concluded that the Clair Oil Field has great potential to increase oil production by combining infill drilling with waterflooding, after other improved oil recovery techniques fail.

Carbon dioxide flooding can accelerate the development of low-permeability reservoirs of the Kexia group in the K region of the T oil field, thus resolving the issue of inadequate water drive effects. This study was focused on the well group 80513 in the K region, and based on the reservoir and fluid parameters, a simulation model of water-sensitive post-CO2 flooding was constructed to refine the gas injection strategy gradually. The injection rate of the continuous gas injection stage was preferred based on the degree of recovery. Multiindicator and multifactor injection and extraction schemes were established to optimize and analyze the key controlling factors, including the gas injection rate, gas injection period, gas-to-water ratio, and bottom-hole flow pressure, in the carbon dioxide gas-to-water alternation process. Recovery efficiency, oil exchange rate, formation pressure, and carbon dioxide storage rate were used as indicators. After 5 years of continuous CO2 flooding, the results indicated that switching to CO2 gas-water alternating flooding was more appropriate for the target block's environment. The best development plan was achieved when the gas injection rates were 1.0 and 1.25 × 104 m3·d-1 for continuous gas injection and CO2 gas-water alternating flooding, respectively, with a gas-water ratio of 1:1, a gas injection cycle of 90 days, and a bottom-hole flow pressure of 25 MPa in the production wells. A comparison between the results revealed that the formation pressure and oil recovery efficiency of this well group significantly increased upon CO2 flooding, and the parameter optimization results were well suited for controlling the gas flurry, offering a versatile model for future development of the block.

SummaryWaterflood optimization by means of rate control is receiving considerable attention because of increasing deployments of smart well completions and i-field technology. The use of inflow control valves (ICVs) allows us to optimize the production/injection rates of various segments along the wellbore, thereby maximizing sweep efficiency and delaying water breakthrough. Field-scale rate-optimization problems, however, involve highly complex reservoir models, production and facility constraints, and a large number of unknowns. In this paper, we propose an approach that is computationally efficient and suitable for large field cases. It is based on our previous work (Alhuthali et al. 2007, 2008), which relies on equalizing arrival time of the waterfront at all producers to maximize the sweep efficiency. We use streamlines to efficiently and analytically compute the sensitivity of the arrival times with respect to well rates. We also account for geologic uncertainty by means of a stochastic optimization framework using multiple realizations. Analytical forms for gradients and Hessian of the objective functions are derived, making our optimization computationally efficient for large-scale applications. Finally, optimization is performed under operational and facility constraints using a sequential quadratic programming approach.We demonstrate our approach using two field-scale examples. The first is a synthetic example called "Brugge" field, a benchmark case based on a North Sea Brent-type field. The production optimization of this field is carried out as part of a closed-loop process where the production history is matched prior to the production optimization. The production optimization is performed over multiple realizations for 20 years and involves 30 wells equipped with three ICVs per well. The second example is a super-giant Middle Eastern field that has more than 50 years of historical oil production. The optimization is performed for 20 years on a portion of this field that contains nearly 300 wells consisting of conventional vertical and horizontal wells and smart horizontal wells. In both examples, multiple field-related constraints are imposed, such as the maximum well injection and production rates, the maximum allowable drawdown, restriction on high-water-cut wells, and voidage replacement for pressure maintenance. The results clearly demonstrate the viability of our approach and the benefits of optimal rate control, with a considerable increase in cumulative oil production and a substantial decrease in the associated water production.

Efficient reservoir sweep is critical for operators to boost oil production in the Middle East. This task becomes particularly challenging in carbonate formations, which typically feature permeability ranging from microscopic pores to large cavernous vugs. Extreme heterogeneity disserves water injectors, leading to nonuniform injection profiles. Consequently, water sweeping is inefficient and leaves significant residual oil behind. In the Mesopotamian Basin, the matrix stimulation approach was rethought to address high permeability contrasts and produce the bypassed oil. The methodology relied on coiled tubing (CT) equipped with fiber optics and real-time downhole measurements, a CT-deployed inflatable packer, and a high-pressure rotary jetting tool. The array of downhole readings was leveraged to ensure optimal use of the bottomhole assembly. The high-pressure rotary jetting tool was used in the first run to condition the wellbore tubulars across the inflatable packer planned anchoring depth. In the second run, the inflatable packer was set at the target depth, and the stimulation treatment was selectively pumped either above or below the packer, depending on the depth of the interval of interest. The proposed stimulation technique was implemented in more than 40 wells, which included vertical and deviated water injectors, completed with 3 1/2-in. or 4 1/2-in. tubing and up to 7-in. casing, with two to five perforated intervals averaging 30 to 50 m in total, temperatures ranging from 90 to 140°F, and an average meadured depth of 2500 m. The CT-deployed inflatable packer had an expansion ratio of up to 3 to 1. CT real-time downhole measurements, such as CT internal pressure, CT annulus pressure, temperature, downhole axial forces, gamma ray, and casing collar locator (CCL), were instrumental to eliminate the uncertainties associated with changing downhole conditions and depth correlation. They also enabled a controlled actuation of the downhole tools in subhydrostatic wells, as the pressure imbalance caused by the low bottomhole pressure can generate loss of fluid flow and pressure across the tools. For the first time, the operator was able to stimulate the tight rock in water injector wells, enhancing injection sweeping efficiency and boosting oil production from offset wells. As a result of this campaign, production gains are estimated at 60,000 BOPD, and injectivity increased in average 2 times per intervention. This approach has now become the state of the practice for the operator to stimulate wells with high permeability contrast. This enhanced matrix stimulation technique, leveraged by CT and real-time downhole measurements, brings a new level of confidence to accurately and effectively deploy inflatable packers in wells with challenging expansion ratio and low reservoir pressure. In addition, the proposed technique enables stimulating tight rock across intervals with extreme heterogeneity, resulting in a more efficient sweep and an increase in oil production.

As part of studying miscible GI in Deep Water GoM, four key areas were identified that could pose risk or uncertainty to value delivery. These were: reservoir containment, injectivity, reservoir sweep and reservoir optimization. Top seal integrity, column capacity, fault seal risk and asphaltene formation risk were identified as key drivers underpinning containment risk. As pointed out by Abouie et al. (2018), deposited asphaltene on the rock causes wettability alteration. This, in turn, significantly changes the displacement dynamics in miscible gas floods by lowering the gas mobility, increasing the mixing length, and reducing the displacement and sweep efficiency. The industry has not conducted many experiments to quantify the impact of asphaltenes on reservoir and well performance under gas injection (GI) conditions. This paper discusses a novel laboratory testing for evaluating the asphaltene impact in a major oil field in GoM. The goals of the study were to: a) Define the Asphaltene Precipitation Envelope (APE) using blends of reservoir fluid and injection gas, and b) Measure permeability reduction due to asphaltene precipitation in a core under gas injection. To properly analyze GI impact, a full suite of fluid characterization were conducted including restoring oil samples, compositional analysis, constant composition expansion and differential vaporization. The miscibility conditions were defined through slim tube displacement tests. Then, gas solubility-swelling tests were conducted. This was followed by Asphaltene Onset Pressure (AOP) testing. The permeability impairment experiments were carried out in a long core. A unique procedure was developed to estimate the impact of asphaltene deposition on core permeability. Using a fully saturated core with the live oil and injection gas mixture, several depletion tests were repeated starting from a pressure above the AOP (9000 psi) down to a pressure just above the bubble point followed by reloading the core with the same mixture at 9000 psi. These tests were meant to mimic continuous flow of oil along the path of injected gas and thereby observe the accumulation of asphaltene on the rock surface. The test results indicated that during this cyclic asphaltene deposition process, the core permeability to the live mixture decreased in the first few cycles but appeared to stabilize after cycle 5. The deposited asphaltenes were analyzed further through Environmental Scanning Electron Miscroscopy (ESEM), and their deposition was confirmed by mass balance before and after the tests. Finally, a relationship was established between permeability reduction and asphaltene precipitation. The results from the asphaltene deposition experiment show that for the sample, fluids, and conditions used, permeability is impaired as asphaltene flocculates and begins to coat the grain surfaces. This impairment reaches a plateau and subsequent permeability measurements indicate that stability is being established at approximately 40 percent of the initial permeability. Distribution of asphaltene along the core was measured at the end by segmenting the core and conducting solvent extraction. A novel systematic approach was developed for characterizing the permeability impairment due to asphaltene deposition during a gas injection EOR process. Numerical modeling of these test results and using this model to forecast the magnitude of the permeability impairment to be expected in a reservoir setting during miscible gas injection is our recommendation.

Carbon capture, utilization and sequestration (CCUS) is one of the most promising and effective technologies to reduce carbon emissions, with significant potential for development in the coming decades. The CCUS coupling project utilizing CO2 Enhanced Oil Recovery (CO2-EOR) can pave the way to economically viable CO2 sequestration processes. However, the non-linear and non-convex relationships among CO2 sequestration capacity, CO2-EOR effectiveness, and economic benefits significantly increase uncertainty in technological policy formulation. This paper establishes a comprehensive optimization framework utilizing the Particle Swarm Optimization (PSO) algorithm, encompassing the formulation of objective functions, the development of numerical simulation models for typical reservoirs, and single-objective and multi-objective optimization method. In single-objective optimization, two objective functions—oil recovery factor and CO2 sequestration efficiency—are accomplished through weight assignment. This approach optimizes the combined objective function across different injection modes, including Continuous gas injection (CGI), Water-alternating-gas (WAG), and Gas injection after water flooding (GIW), while also considering diverse preferences for development strategies. In multi-objective optimization, the optimal well control parameter sets for CO2 sequestration and Net Present Value (NPV) are derived under different injection modes using Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and the Pareto front. The results indicate that the multi-objective optimization method constructed in this paper can effectively address the problem of Co-Optimization of CO2-EOR projects. Compared to the base scenario and the single-objective optimization, the MOPSO optimization solutions on the Pareto front demonstrate superior overall performance in terms of oil recovery, sequestration capacity, and economic benefits through reasonable injection and production parameters that control the CO2 flooding front and expand the sweep efficiency. Water-Alternating-Gas (WAG) exhibits advantages in recovery rate and NPV while demonstrating lower sensitivity to carbon subsidies and CO2 injection costs. The comprehensive optimization method proposed in this paper will significantly enhance the technical decision-making capabilities related to CCUS coupling projects in carbonate reservoirs, providing guidance for safe and efficient CCUS technologies.