|Theme||Visible||Selectable||Appearance||Zoom Range (now: 0)|
Summary Recent studies have indicated that huff ‘n’ puff (HNP) gas injection has the potential to recover an additional 30 to 70% oil from multifractured horizontal wells in shale reservoirs. Nonetheless, this technique is very sensitive to production constraints and is impacted by uncertainty related to measurement quality (particularly frequency and resolution) and lack of constraining data. In this paper, a Bayesian workflow is provided to optimize the HNP process under uncertainty using a Duvernay shale well as an example. Compositional simulations are conducted that incorporate a tuned pressure/volume/temperature (PVT) model and a set of measured cyclic injection/compaction pressure‐sensitive permeability data. Markov‐Chain Monte Carlo (MCMC) is used to estimate the posterior distributions of the model uncertain variables by matching the primary production data. The MCMC process is accelerated by using an accurate proxy model (kriging) that is updated using a highly adaptive sampling algorithm. Gaussian processes are then used to optimize the HNP control variables by maximizing the lower confidence interval (μ‐σ) of cumulative oil production (after 10 years) across a fixed ensemble of uncertain variables sampled from posterior distributions. The uncertain variable space includes several parameters representing reservoir and fracture properties. The posterior distributions for some parameters, such as primary fracture permeability and effective half‐length, are narrower, whereas wider distributions are obtained for other parameters. The results indicate that the impact of uncertain variables on HNP performance is nonlinear. Some uncertain variables (such as molecular diffusion) that do not show strong sensitivity during the primary production strongly impact gas injection HNP performance. The results of optimization under uncertainty confirm that the lower confidence interval of cumulative oil production can be maximized by an injection time of approximately 1.5 months, a production time of approximately 2.5 months, and very short soaking times. In addition, a maximum injection rate and a flowing bottomhole pressure around the bubblepoint are required to ensure maximum incremental recovery. Analysis of the objective function surface highlights some other sets of production constraints with competitive results. Finally, the optimal set of production constraints, in combination with an ensemble of uncertain variables, results in a median HNP cumulative oil production that is 30% greater than that for primary production. The application of a Bayesian framework for optimizing the HNP performance in a real shale reservoir is introduced for the first time. This work provides practical guidelines for the efficient application of advanced techniques for optimization under uncertainty, resulting in better decision making.
Olusola, Bukola Korede (Schulich School of Engineering, University of Calgary) | Orozco, Daniel (Schulich School of Engineering, University of Calgary) | Aguilera, Roberto (Schulich School of Engineering, University of Calgary)
Recent improved and enhanced oil recovery (IOR and EOR) methods in shale reservoirs use huff and puff gas injection (H&P). Investigating the technical and economic impact of this technology for one well is challenging and time consuming. Even more so when the petroleum company is planning H&P and refracturing (RF) jobs in multiple wells. Thus, in this paper we present an original methodology to learn how to perform these tasks faster and at lower cost to improve oil recovery.
The procedure is explained with the use of an actual H&P gas injection pilot horizontal well in the Eagle Ford shale whose performance is matched using the methodology developed in this paper. The methodology includes use of an original Climbing Swarm (CS) derivative-free algorithm that drives, without human intervention, computer or laptop material balance (MatBal) and net present value (NPV) calculations. The code was written in Python. Following history match, the methodology demonstrates that significant improvements in oil recovery can be obtained by injecting gas at larger rates during shorter periods of time (as opposed to injecting gas at smaller rates during longer periods of time).
Once oil recovery improvement in a pilot horizontal well is demonstrated, the methodology is extended to the analysis of H&P gas injection and refracturing in horizontal wells and shale reservoirs that have not yet been developed or are in initial stages of development; this provides a preliminary assessment of H&P and refracturing potential.
Results indicate that oil recovery and NPV from multiple wells can be improved significantly by a strategic combination of H&P gas injection and refracturing. Combination of derivative-free optimization algorithms, MatBal calculations and net present value permits optimizing when to start the H&P gas injection project, the optimum gas injection rates and time-span of injection, reservoir pressure at which gas injection should be initiated in each cycle, and the time-span during which the well should produce oil, previous to starting a new cycle of gas injection. The development strategy of shale oil reservoirs could be improved significantly if the possibility of H&P gas injection is considered previous to field development. This could be the case of the Eagle Ford shale in Mexico, La Luna shale in Colombia and Venezuela, Vaca Muerta shale in Argentina and other shale oil reservoirs worldwide.
The paper contributes the development of an original methodology, which includes use of a derivative free algorithm we call "Climbing Swarm (CS)." CS drives the computer or laptop to perform MatBal and NPV calculations, without human intervention, once the optimization process is started. The methodology improves oil recovery and NPV from a single horizontal well or from multiple horizontal wells operating under H&P gas injection.
Abstract While CO2 flooding is expected to increase oil recovery, deviations of actual production from predicted values add significant challenges when optimizing flood design under uncertain conditions. The aim of this paper is to introduce a comprehensive optimization process with uncertainty analysis to obtain a more plausible decision for a field application scenario. In this paper, a comprehensive optimization process is developed to optimize the production performance of entire production lifespan for a CO2-WAG EOR process in Pubei reservoir, Turpan-Hami Basin. Start times of the waterflooding and CO2 WAG proess (i.e., durations of the primary production and waterflooding) are also included in the optimization process as well as the producer's bottomhole pressures and injection rates, in addition to the water and gas injection rates for the WAG process, WAG ratio, and well bottomhole pressures at the producers. The comparison is then performed between the conventional WAG optimization processes with the comprehensive optimization process. A total of 80 reservoir realizations is generated and history-matched to consider the impacts of the geological uncertainty on the optimization process. Finally, the reliability of this optimization design is quantified under the geological uncertainty. Results from a deterministic comprehensive optimization design demonstrate that the oil recovery and NPV of the optimized CO2-WAG process are increased by 23.4% and 51.3%, respectively, in comparison to the optimal case obtained by the conventional WAG optimization process. After incorporating uncertainties into the geological model, the distributions of oil recovery and NPV, including P10, P50, P90 are quantified. Based on uncertainty assessment, it is found that the optimized CO2-WAG scheme is a reliable scheme for the reservoir development. This paper provides quantitative insights on the significance of both geological and operational factors on the reliability of optimal design over the entire life span of a CO2 WAG operation. It is expected that the integrated workflow will help operators to optimize well performance more efficiently and predict production performances with higher reliability.
Abstract Many Steam Assisted Gravity Drainage (SAGD) optimization studies published in the literature combined numerical simulation with graphical or analytical techniques for design and performance evaluation. There have been numerous efforts that integrated the simulation exercise with global optimization algorithms. Some studies focused on optimization of cumulative steam-to-oil ratio (cSOR) in SAGD by altering steam injection rates, while others focused on optimization of cumulative net energy-to-oil ratio (cEOR) in solvent-additive SAGD by altering injection pressures and fraction of solvent in the injection stream. Several studies also considered total project net present value calculation by changing total project area, capital cost intensities, solvent prices, and risk factors to determine the well spacing and drilling schedule. Optimization techniques commonly used in those studies were scattered search, simulated annealing, and genetic algorithm (GA). However, the applications of hybrid genetic algorithm were rarely found. In this paper, we focused on optimization of solvent-assisted SAGD using various GA implementations. In our models, hexane was selected to be co-injected with steam. The objective function, defined based on cumulative steam-oil ratio (cSOR) and recovery factor, was optimized by changing injection pressures, production pressures, and injected solvent-to-steam ratio. Techniques including orthogonal arrays (OA) for experimental design (e.g. Taguchi’s arrays) and proxy models for objective function evaluations were incorporated with the GA method to improve computational and convergence efficiency. Results from these hybrid approaches revealed that an optimized solution could be achieved with less CPU time (e.g. fewer number of iterations) compared to the conventional GA method. Sensitivity analysis was also conducted on the choice of proxy model to study the robustness of the proposed methods. To investigate the effects of heterogeneity in the design process, optimization of solvent-assisted SAGD was performed on various synthetic heterogeneous reservoir models of porosity, permeability, and shale distributions. Our results highlight the potential application of the proposed techniques in other solvent-enhanced heavy oil recovery processes.
Summary The depletion of petroleum hydrocarbons in the world has progressively increased during the past decades. To meet the increasing demand for energy resources, improving the oil recovery of unconventional reservoirs, such as shale oil has appealed great attention. The Eagle Ford shale reservoir is one of the most active U.S. shale plays. An article by Hart (2011) quoted one of EOG resources recent reports that recovery factor for the Eagle Ford shale play during primary drive reservoir depletion will be roughly 5%. Other articles stated that the recovery factor of these reservoirs could be between 5 to 15 %. The vast oil remaining in shale reservoir stimulates our efforts to investigate the application of enhanced oil recovery methods. The main aid of this simulation study is to examine the feasibility of cyclic natural gas injection. Cyclic natural gas injection can be applied as substitution of cyclic CO2 injection with great advantages, such as lower corrosion levels and availability of the natural gas in some of shale oil plays such as Eagle ford shale reservoirs. In this paper, the effects of soaking period and injection pressure on oil recovery, among other parameters, were investigated. The compositional simulation study results show that cyclic natural gas injection is able to increase the recovery factor from 3 % to 5% depending on the operating conditions and number of cycles. In general, this study aided to develop a better understanding of the performance of cyclic natural gas injection in shale oil reservoirs.