Modeling and analysis of temperature transients caused by step-like change of downhole flow control device flow area

Abstract Evolution and wide-spreading of downhole monitoring systems has increased a lot over the last few years. Particularly downhole temperature data has been recognized as an important input for real-time production optimization. Whereas distributed temperature analysis and interpretation are the only methods used now, temperature evolution with time offers an excellent new source of information not yet fully understood. Simulation and analysis of temperature transients in wells are therefore a promising research area at the initial stage of development. Near wellbore multiphase flow simulation considering heat transfer is not a simple task. There are aspects such as wellreservoir mutual influence where transient analysis needs to be considered. Neglecting thermal effects and completion design will result in inaccuracies of the physical phenomena representation. This paper describes a non-isothermal dynamic well-reservoir simulator taking into account the presence of flow control valves in the wellbore. A fully coupled approach was used to solve the problem. The proposed model studied is two dimensional for the reservoir and one dimensional for the well. The reservoir is considered homogeneous, anisotropic and two -phase (oil/water or gas/liquid) saturated with the initial pressure above the bubble point. The well is vertical and the multiphase drift-flux model considering the radial influx is used to describe the flow in the wellbore. Case studies for three-zone intelligent wells are analyzed from transient temperature profiles generated by the simulator. A single phase is used as a base case for the two-phase case. This paper shows that temperature transients from a step-change in production rate are able to provide full well-test equivalent information about each production zone. A downhole flow control valve step-change is also analyzed as a feasibility study of a well test without zonal shut-in. Restrictions of the simulator are presented and discussed for an appropriate use of the results.</jats:p

Novel Solutions and Interpretation Methods for Transient, Sandface Temperature in Vertical, Dry Gas Producing Wells

Abstract Pressure-Transient-Analysis (PTA) is a technique that is routinely used by both Production and Reservoir Engineers for a variety of applications during the lifetime of a producing well. It is initially used for reservoir characterisation and, later on, for well performance monitoring and (wider) reservoir surveillance. The application of high precision, downhole, temperature sensors has resulted in PTA being complemented or replaced by Temperature-Transient-Analysis (TTA). Recent TTA research has shown that comprehensive information on the state of the near-wellbore zone and fluid flow rates and composition can potentially be derived from such measurements. However, the derivation and use of TTA solutions is challenging, due to both the small value of the measured temperature change and the more complex nature of the governing physics and equations. In particular, analysis workflows for wells producing gas or gas-liquid mixtures are still lacking since most published liquid TTA solutions cannot be applied in the presence of gas. This paper addresses the missing workflow for a (dry) gas producing well. It presents the derivation of novel analytical transient sandface temperature solutions together with the development and application of workflows for interpreting transient sandface temperature data in vertical, dry gas wells. Estimation of either the flow rate or the permeability. thickness (kh) product is demonstrated using a linearized analytical solution. Further, the radius and permeability of a near-wellbore zone of reduced permeability (due to formation damage) can be determined by tracking changes in the temperature transient using the thermal radius of investigation concept. The complete interpretation methodology has been validated by a bespoke numerical model and its application illustrated by case studies. The developed solution and workflows provides a simple and fast method for interpreting sandface temperature data. They will be invaluable for well testing and monitoring applications. It is a major step forward in the longer-term project of developing a full-spectrum of TTA methods for multiphase (gas-liquid) producing wells.</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

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

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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

Bridging the Performance Gap between Passive and Autonomous Inflow Control Devices with a Hybrid Dynamic Optimization Technique Integrating Machine Learning and Global Sensitivity Analysis

Wells equipped with flow control devices across their completion intervals have become a proven field development option for geologically complex and/or viscous oil reservoirs. Such wells increase oil recovery, reduce water and gas production, minimize the need for well workover operations, and subsequently lower the wells' carbon footprint. The uncontrolled types of inflow control devices include early-generation passive inflow control devices (ICDs) and later-generation autonomous inflow control devices (AICDs). The superior performance of AICDs over ICDs in managing water and gas production, as well as enhancing the overall well and reservoir performance has been demonstrated in multiple research and case studies. This superiority stems from the AICDs’ ability to self-adjust and increase their flow resistance when undesired fluids (i.e., water and/or gas) flow through them. While ICDs lack this self-adjusting feature, they are more affordable and more readily available on the market. This study aims to reduce the performance gap between passive and autonomous inflow control devices by developing a hybrid dynamic optimization technique. This approach integrates a metaheuristic algorithm, machine learning, global sensitivity analysis, and correlation measures to facilitate the optimization problem by identifying the high-impact control variables. Next, the proposed workflow finds the necessary adjustments to the original well completion design by modifying the high-impact control variables during the optimization process. This results in a modified well completion design that is less influenced by the type of inflow control device (passive or autonomous), thereby bridging the performance gap between these two completion types. The study employs a benchmark ‘Egg field’ model, featuring two multilateral wells (MLWs) producing under a water flooding recovery mechanism. Two different completion designs, utilizing either ICDs or AICDs, are optimized using standard optimization (SO) and the proposed hybrid dynamic optimization techniques. The standard optimization, which employs a standalone Particle Swarm Optimization (PSO) algorithm, highlights, as expected, the superiority of the AICD-based completion, yielding an approximately 13% increase in the net present value (NPV) over the ICD-based completion. However, when applying the hybrid optimization (HO) technique, this difference is significantly reduced to 3.4%. This indicates the potential for the hybrid optimization technique to make ICD-based completions more competitive and economically favourable compared to their AICD-based counterparts