Self-Learning Reservoir Management

Saputelli, Luigi; Nikolaou, Michael; Economides, Michael J.

doi:10.2118/84064-pa

Cited by 58 publications

(18 citation statements)

References 30 publications

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“…However, also in that case, the short-term production strategy will most likely not be determined a priori using only simulation results, but rather in real time, using production measurements, while constrained by limits to ensure that the long-term reservoir-management objectives are also met. Such multilevel control concepts were described by De et al (2000), Nyhavn et al (2000), and Saputelli et al (2003). However, until closed-loop approaches are matured, production optimization in reality will remain a short-term-focused activity, aimed at maximizing oil production while satisfying production constraints.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

Naus

Dolle

2006

SPE Production &Amp; Operations

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We developed an operational strategy for commingled production with infinitely variable inflow control valves (ICVs) using sequential linear programming (SLP). The optimization algorithm requires instantaneous and derivative information. We propose a workflow in which the production engineer relies on measurements to determine the flow rate and pressure values and on models to determine the derivative information (i.e., the changes in flow rates as a result of a change in an ICV setting). Such a model typically would be a steady-state wellbore simulator including choke models to represent the ICVs and inflow models to represent the near-well reservoir flow in the various zones. The parameters of the model need to be updated regularly using real-time measurements and production tests, and we discuss the impact of different smart-well instrumentation levels on the updating process.We simulated the performance of this production-optimization strategy in a reservoir simulator. Some numerical aspects of the algorithm and problems encountered during implementation are discussed. The performance of the algorithm was tested in two reservoir settings. In both cases, the optimization resulted in accelerated oil production compared to conventional, surfacecontrolled production. However, accelerated production did not always result in higher ultimate recovery compared to the conventional case. In such situations, the benefits of either short-term production optimization (accelerating production) or long-term reservoir management (maximizing recovery) should be weighed.Production Optimization With Smart Wells. The methods discussed above rely on a reservoir model, which will always contain geological uncertainties, so that the predicted reservoir response

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

Naus

Dolle

2006

SPE Production &Amp; Operations

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“…Great effort has been devoted to constructing high-order reservoir models for improved oil recovery [1,2]. Unfortunately, the accurate characterization of certain parameters in the simulation, such as rock porosity and permeability, is a difficult task to be performed, requiring large amounts of computational effort and storage [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, they were basically off-line applications of optimal control theory that do not explicitly use real-time data computations. Real-time, modelbased control applications have been introduced in the literature, recently for oil recovery [3]. One of the fundamental difficulties in designing such controllers for large-scale reservoir management stems from the fact that reservoir simulation models, given by PDE's with highly heterogeneous system parameters, yield large state-space dimensions (on the order of tens of thousands to millions) upon discretizations and, in turn, large dimensions for model-based controllers.…”

Section: Introductionmentioning

confidence: 99%

Development of Low-Order Controllers for High-Order Reservoir Models and Smart Wells

Gildin¹,

Klíe

Rodríguez

et al. 2006

SPE Annual Technical Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractRecently, the oil industry has started instrumenting and deploying various controls for the enhancement of hydrocarbon extraction. However, due to system complexity, data acquisition and, in turn, decision making, are still issues to be resolved in large-scale reservoir management. A very challenging large-scale control problem that has emerged recently is the real-time control of smart wells. A fundamental reason for using feedback control in this setting is to achieve desired performance in the presence of external disturbances and model uncertainties.This work introduces iterative Krylov subspace projection (KSP) methods to generate low-order feedback controllers for smart wells employing high-order reservoir models. The main motivation for using these methods is to enable the efficient computation of low-order reservoir models derived from the highly sparse structure of fluxes and pressure coefficients after discretization. Compared to other model reduction methods, such as modal decomposition, balanced realization, subspace identification and the proper orthogonal decomposition (POD), the proposed approach is very efficient since it is fundamentally based on sparse matrix-vector products. For a problem size of n simulation gridblocks, the KSP method presents a complexity of O(n 2 k) (or even O(nk), by exploiting sparsity), where k is the number of iterations required to generate the low-order model. This is highly desirable for the design of timely low-controller mechanisms in smart wells.We illustrate the potential of the method by linearizing an oil-water model and showing how to establish stability and error bounds for production responses under certain perturbations. We also discuss how the method can be extended to cases of non-stationary and nonlinear fluid coefficients.

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“…To maintain the desired operating conditions, this type of feedback controller is frequently used in production and refining processes in the petroleum industry (Wang et al 2000;Havre and Dalsmo 2001;van Dijk et al 2008;Bieker et al 2007;Sengul and Bekkousha 2002;Stenhouse 2006;Oberwinkler and Stundner 2005;Saputelli et al 2005;Nikolaou et al 2006;Guyaguler 2009). The control systems implemented in facilities and refineries have sensors and mechanical equipment to observe and physically modify the process settings.…”

Section: Proportional-integral-derivative (Pid) Controllermentioning

confidence: 99%

Smart De-Watering and Production System through Real-Time Water Level Surveillance for Coal-Bed Methane Wells

Han

Ling

2015

SPE Digital Energy Conference and Exhibition

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In the past few decades, Coal-Bed Methane (CBM) has become an important source of energy especially in North America. The methane adsorbed within the coal is in a near-liquid state. The open fractures in the cleats are commonly saturated with water. In developing the CBM reservoir, water in the fracture spaces and coal seam must be continuously pumped off from coal seam. This reduces pressure and desorbs gas from matrix.Although operators desire to produce hydrocarbon quickly, a too fast dewatering rate can irreversibly damage matrix desorption process, which can lead to an unfavorable ultimate recovery. Further, the aggressive production rate can potentially release the coal fines and drive them into pump, which increases maintenance effort and cost. Even worse, the de-watering process fluctuates because of the rock porosity and permeability changes resulting from the brittle coal seam and pressure reduction. Therefore, it is critical to adjust the pump operating parameters in a timely manner to maintain a continuous/intermittent production.The 10 CBM wells at study locate in a mountain with difficult access. Previously engineers had to evaluate well performance and optimize the pump on-site, which is limited by a monthly basis. We firstly developed an automatic data processing system using the advanced Echosounders, which can measure the water level in real time. The reservoir pressure can be then monitored dynamically through interpreting the detected water level. With an automatic-wireless data transferring system installed on-site and a closed-loop control program to receive, process, and interpret data, the pump operating parameters can be changed in real time through remote control. This system not only identifies the downhole problems in real time, but also reduces the pump maintenance frequency from 40 days to 75 days statistically and numbers of trip to well site. Further, the gas production rate has been averagely improved by 30% for the 10 wells.The authors firstly developed an automation data processing and control system in the favor of advanced echosounders. Based on the interpreted reservoir pressure, we can avoid aggressive production by adjusting the pump operating parameters in real time, which eventually results in a better ultimate recovery. The developed workflow (automatic echosounder data acquisition, real filed data transferred to central office, data processing, interpretation, and simulation in computational system, adjustment commands to operating system) is especially valuable for the locations difficult to access.

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Self-Learning Reservoir Management

Cited by 58 publications

References 30 publications

Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

Optimization of Commingled Production Using Infinitely Variable Inflow Control Valves

Development of Low-Order Controllers for High-Order Reservoir Models and Smart Wells

Smart De-Watering and Production System through Real-Time Water Level Surveillance for Coal-Bed Methane Wells

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