Fluid Injection Optimization Using Modified Global Dynamic Harmony Search

Abstract One of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. Several parameters should be optimized in a fluid injection process. The usual optimizing methods evaluate several scenarios to find the best solution. Since it is required to run the reservoir simulator hundreds of times, the process is very time consuming and cumbersome. In this study a new intelligent method of optimization, called "global dynamic harmony search" is used with some modifications in combination with a commercial reservoir simulator (ECLIPSE ® ) to determine the optimum solution for fluid injection problem unknowns. Net present value (NPV) is used as objective function to be maximized. First a simple homogeneous reservoir model is used for validating the developed method and then the new optimization method is applied to a real model of one of the Iran oil reservoirs. Three strategies, including gas injection, water injection, and well placement are considered. Comparing the values of NPV and field oil efficiency (FOE) of gas injection and water injection strategies, it is concluded that water injection strategy surpasses its rival. Considering water injection to be the base case, a well placement optimization is also done and best locations for water injection wells are proposed. The results show the satisfying performance of the algorithm regarding its low iterations

A robust, multi-solution framework for well location and control optimization

Optimal field development and control aim to maximize the economic profit of oil and gas production while considering various constraints. This results in a high-dimensional optimization problem with a computationally demanding and uncertain objective function based on the simulated reservoir models. The limitations of many current robust optimization methods are: 1) they optimize only a single level of control variables (e.g. well locations only; or well production/injection scheduling only) that ignores the interferences between control variables from different levels; and 2) they provide a single optimal solution, whereas operational problems often add unexpected constraints that result in adjustments to this optimal solution scenario degrading its value. This paper presents a robust, multi-solution framework based on sequential iterative optimization of control variables at multiple levels. Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is used as the optimizer while the estimated gradients are calculated using a 1:1 ratio mapping ensemble of control variables perturbations at each iteration onto the ensemble of selected reservoir model realizations. An ensemble of closeto- optimum solutions is then chosen from each level (e.g. from the well placement optimization level) and transferred to the next level of optimization (e.g. where the control settings are optimized), and this loop continues until no significant improvement is observed in the expected objective value. Fit-for-purpose clustering techniques are developed to systematically select an ensemble of realizations to capture the underlying model uncertainties, as well as an ensemble of solutions with sufficient differences in control variables but close-to-optimum objective values, at each optimization level. The proposed framework has been tested on the Brugge benchmark field case study. Multiple solutions are obtained with different well locations and control settings but close-to-optimum objective values, providing the much-needed operational flexibility to field operators. We also show that suboptimal solutions from an early optimization level can approach and even outdo the optimal one at the next level(s) demonstrating the advantage of the developed framework in a more efficient exploration of the search space

Application of Remote Sensing In Two Southern Iranian Oil Fields

Geoscientists have long applied photographic cameras, radar, lasers, infrared (IR) scanners, radiometers, spectrometers, microwaves, and multi spectral scanners (MSS) in the search for hydrocarbons. With introduction of satellite remote sensing, basic techniques were then coupled with this new technology. This produced enhanced views of the Earth’s surface. Although oil and gas reservoirs are deep below the surface, they have some indicators, which can be detected on the ground. To reduce the exploration costs for hydrocarbons during the reconnaissance stage of exploration, satellite images and available surface data by combining with other current conventional exploration techniques could be used. In recent years, geological reconnaissance has been augmented by sophisticated terrace data-gathering techniques, which have been categorized as remote sensors. GIS allows petroleum engineers or functional group within to communicate information and make spatial and temporal decisions about assets, activities and natural resources. The present paper deals with the study of two existing petroleum-rich reservoirs. The selected area contains thermally unprocessed VNIR, SWIR and TIR ASTER images for granule of the study area covering Ab-teymur and Darquin reservoirs. Each granule covers an area of 3600 Km2 (60 km x 60 km) of land of onshore Iran. Besides the main geological units and the gas geological analysis within the boundary of these granules have been studied. For this work three layers of information are considered: geology, geochemistry and vegetation cover. The main geological units within the boundary of the granules have been discussed for both fields. The basis of gas geochemical prospecting methods is that no oil or gas reservoir cap rock is completely impermeable. Hydrocarbons and other compounds and elements escape from the reservoirs and the more volatile components migrate to the surface where they may be trapped in soils or diffuse in atmosphere or ocean. Vegetation cover within the boundaries of oil field influenced zones was taken into consideration as an individual layer of information which will complement the other layers of information by its corresponding statistical weight

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

Tutkijayrittäjyydestä on tullut yhä toivottavampi kehityssuunta suomalaisten korkeakoulujen keskuudessa muun innovaatiotoiminnan lisäksi. Innovaatiopuhe ja yrittäjyys ovat olleet jo pitkään suomalaisen akateemisen yhteisön hampaissa ja nostattaneet kritiikkiä yliopistoyhteisössä. Muun muassa Tampereen yliopiston kevään 2020 ulostulot herättivät tutkijat, jotka vaativat loppua ”pöhinäretoriikalle”. Mikä kaikki yrittäjyyspuheessa kavahduttaa tutkijoita? Tämä keskustelunavaus liittää yhteen keskustelut tutkijayrittäjyydestä ja kapitalismin puutteista

Proactive Optimization of Intelligent-Well Production Using Stochastic Gradient-Based Algorithms

Summary The popularity of intelligent wells (I-wells), which provide layer-by-layer monitoring and control capability of production and injection, is growing. However, the number of available techniques for optimal control of I-wells is limited (Sarma et al. 2006; Alghareeb et al. 2009; Almeida et al. 2010; Grebenkin and Davies 2012). Currently, most of the I-wells that are equipped with interval control valves (ICVs) are operated to enhance the current production and to resolve problems associated with breakthrough of the unfavorable phase. This reactive strategy is unlikely to deliver the long-term optimum production. On the other side, the proactive-control strategy of I-wells, with its ambition to provide the optimum control for the entire well's production life, has the potential to maximize the cumulative oil production. This strategy, however, results in a high-dimensional, nonlinear, and constrained optimization problem. This study provides guidelines on selecting a suitable proactive optimization approach, by use of state-of-the-art stochastic gradient-approximation algorithms. A suitable optimization approach increases the practicality of proactive optimization for real field models under uncertain operational and subsurface conditions. We evaluate the simultaneous-perturbation stochastic approximation (SPSA) method (Spall 1992) and the ensemble-based optimization (EnOpt) method (Chen et al. 2009). In addition, we present a new derivation of the EnOpt by use of the concept of directional derivatives. The numerical results show that both SPSA and EnOpt methods can provide a fast solution to a large-scale and multiple I-well proactive optimization problem. A criterion for tuning the algorithms is proposed and the performance of both methods is compared for several test cases. The used methodology for estimating the gradient is shown to affect the application area of each algorithm. SPSA provides a rough estimate of the gradient and performs better in search environments, characterized by several local optima, especially with a large ensemble size. EnOpt was found to provide a smoother estimation of the gradient, resulting in a more-robust algorithm to the choice of the tuning parameters, and a better performance with a small ensemble size. Moreover, the final optimum operation obtained by EnOpt is smoother. Finally, the obtained criteria are used to perform proactive optimization of ICVs in a real field.</jats:p

Completion Performance Evaluation in Multilateral Wells Incorporating Single and Multiple Types of Flow Control Devices Using Grey Wolf Optimizer

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integrating various types of flow control devices within multilateral wells, employing dynamic optimization process using numerical reservoir simulator while the Grey Wolf Optimizer (GWO) is used as optimization algorithm. The Egg benchmark reservoir model is utilized and developed with two dual-lateral wells. These wells serve as the foundation for implementing and testing 22 distinct completion cases considering single-type and multiple types of flow control devices under reactive and proactive management strategies. This comprehensive investigation aims to shed light on the advantages and limitations of these innovative completion methods in optimizing well and reservoir performance. Our findings revealed that the incorporation of multiple types of FCDs in multilateral well completions significantly enhance well performance and can surpass single-type completions including ICDs or AICDs. However, this enhancement depends on the type of the device implemented inside the lateral and the control strategy that is used to control the ICVs at the lateral junctions. The best performance of multiple-type FCD-based completion was achieved through combining AICDs with reactive ICVs which achieved around 75 million USD profit. This represents 42% and 22% increase in the objective function compared to single-type ICDs and AICDs installations, respectively. The optimal settings for ICD and AICD in individual applications may significantly differ from the optimal settings when combined with ICVs. This highlights a strong correlation between the different devices (control variables), proving that using either a common, simplified analytical, or a standard sequential optimization approach that do not explore this inter-dependence between devices would result in sub-optimal solutions in such completion cases. Notably, the ICV-based completion, where only ICVs are installed with lateral completion, demonstrated superior performance, particularly when ICVs are reactively controlled, resulting in an impressive 80 million USD NPV which represents 53% and 30% increase in the objective function compared to single-type ICDs and AICDs installations, respectively