scholarly journals Life-cycle production optimization of hydrocarbon fields: thermoeconomics perspective

2019 ◽  
Vol 3 (11) ◽  
pp. 3050-3060 ◽  
Author(s):  
R. Farajzadeh ◽  
S. S. Kahrobaei ◽  
A. H. de Zwart ◽  
D. M. Boersma
Keyword(s):  
Life Cycle ◽  
Production Optimization ◽  
Hydrocarbon Fields ◽  
Life Cycle Optimization

Life-cycle optimization of waterfloods leads to reduced CO2 emission and increased profit.

Hierarchical Long-Term and Short-Term Production Optimization

SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 191-199 ◽  
Author(s):  
G.M.. M. van Essen ◽  
P.M.J.. M.J. Van den Hof ◽  
J.D.. D. Jansen
Keyword(s):  
Life Cycle ◽  
Optimization Problem ◽  
Primary Objective ◽  
Production Optimization ◽  
Cycle Performance ◽  
Short Term ◽  
Secondary Objective ◽  
Life Cycle Optimization ◽  
Term Performance

Summary Model-based dynamic optimization of oil production has a significant potential to improve economic life-cycle performance, as has been shown in various studies. However, within these studies, short-term operational objectives are generally neglected. As a result, the optimized injection and production rates often result in a considerable decrease in short-term production performance. In reality, however, it is often these short-term objectives that dictate the course of the operational strategy. Incorporating short-term goals into the life-cycle optimization problem, therefore, is an essential step in model-based life-cycle optimization. We propose a hierarchical optimization structure with multiple objectives. Within this framework, the life-cycle performance in terms of net present value (NPV) serves as the primary objective and shortterm operational performance is the secondary objective, such that optimality of the primary objective constrains the secondary optimization problem. This requires that optimality of the primary objective does not fix all degrees of freedom (DOF) of the decision variable space. Fortunately, the life-cycle optimization problem is generally ill-posed and contains many more decision variables than necessary. We present a method that identifies the redundant DOF in the life-cycle optimization problem, which can subsequently be used in the secondary optimization problem. In our study, we used a 3D reservoir in a fluvial depositional environment with a production life of 7 years. The primary objective is undiscounted NPV, while the secondary objective is aimed at maximizing shortterm production. The optimal life-cycle waterflooding strategy that includes short-term performance is compared to the optimal strategy that disregards short-term performance. The experiment shows a very large increase in short-term production, boosting first-year production by a factor of 2, without significantly compromising optimality of the primary objective, showing a slight drop in NPV of only -0.3%. Our method to determine the redundant DOF in the primary objective function relies on the computation of the Hessian matrix of the objective function with respect to the control variables. Although theoretically rigorous, this method is computationally infeasible for realistically sized problems. Therefore, we also developed a second, more pragmatic, method relying on an alternating sequence of optimizing the primary-and secondary-objective functions. Subsequently, we demonstrated that both methods lead to nearly identical results, which offers scope for application of hierarchical long-term and short-term production optimization to realistically sized flooding-optimization problems.

A Two-Level Strategy to Realize Life-Cycle Production Optimization in an Operational Setting

SPE Journal ◽  
2013 ◽  
Vol 18 (06) ◽  
pp. 1057-1066 ◽  
Author(s):  
G.M. M van Essen ◽  
P.M.J.. M.J. Van den Hof ◽  
J.-D.. -D. Jansen
Keyword(s):  
Life Cycle ◽  
Reference Values ◽  
Large Scale ◽  
Production Optimization ◽  
Data Driven ◽  
Model Errors ◽  
Reservoir Model ◽  
Life Cycle Optimization ◽  
Upper Level

Summary We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level, these profiles are considered as set points (reference values) for a tracking control algorithm, also known as a model predictive controller (MPC), to optimize the production variables over a short moving horizon on the basis of a simple data-driven model. In the process industry such a two-level approach is a well-known strategy to correct for small local disturbances that may have a negative (cumulative) effect on the long-term production strategy. We used a conventional reservoir simulator with gradient-based optimization functionality to perform the life-cycle optimization. Next, we applied this long-term strategy to a reservoir model, representing the truth, with somewhat different geological characteristics and near-wellbore characteristics not captured in the reservoir model used for the longterm optimization. We compared the performance (oil recovery) of this truth model when applying the life-cycle strategy with and without the corrections provided by the data-driven algorithm and the tracking controller. In this theoretical study we observed that the use of the lower-level controller enabled successful tracking of the reference values provided by the upper-level optimizer. In our example, a performance drop of 6.4% in net present value (NPV), caused by differences between the reservoir model used for life-cycle optimization and the true reservoir, was successfully reduced to only 0.5% when applying the two-level strategy. Several studies have demonstrated that model-based life-cycle production optimization has a large scope to improve long-term economic performance of waterflooding projects. However, because of uncertainties in geology, economics, and operational decisions, such life-cycle strategies cannot simply be applied in reality. Our two-level approach offers a potential solution to realize life-cycle optimization in an operational setting.

Robust Life-Cycle Production Optimization With a Support-Vector-Regression Proxy

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2409-2427 ◽  
Author(s):  
Zhenyu Guo ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Simulation Model ◽  
Support Vector Regression ◽  
Reservoir Simulation ◽  
Production Optimization ◽  
Forward Model ◽  
Support Vector ◽  
Well Control ◽  
Steepest Ascent ◽  
Reservoir Simulation Model

Summary We design a new and general work flow for efficient estimation of the optimal well controls for the robust production-optimization problem using support-vector regression (SVR), where the cost function is the net present value (NPV). Given a set of simulation results, an SVR model is built as a proxy to approximate a reservoir-simulation model, and then the estimated optimal controls are found by maximizing NPV using the SVR proxy as the forward model. The gradient of the SVR model can be computed analytically so the steepest-ascent algorithm can easily and efficiently be applied to maximize NPV. Then, the well-control optimization is performed using an SVR model as the forward model with a steepest-ascent algorithm. To the best of our knowledge, this is the first SVR application to the optimal well-control problem. We provide insight and information on proper training of the SVR proxy for life-cycle production optimization. In particular, we develop and implement a new iterative-sampling-refinement algorithm that is designed specifically to promote the accuracy of the SVR model for robust production optimization. One key observation that is important for reservoir optimization is that SVR produces a high-fidelity model near an optimal point, but at points far away, we only need SVR to produce reasonable approximations of the predicting output from the reservoir-simulation model. Because running an SVR model is computationally more efficient than running a full-scale reservoir-simulation model, the large computational cost spent on multiple forward-reservoir-simulation runs for robust optimization is significantly reduced by applying the proposed method. We compare the performance of the proposed method using the SVR runs with the popular stochastic simplex approximate gradient (StoSAG) and reservoir-simulations runs for three synthetic examples, including one field-scale example. We also compare the optimization performance of our proposed method with that obtained from a linear-response-surface model and multiple SVR proxies that are built for each of the geological models.

Systematic MultiObjective Life Cycle Optimization Tools Applied to the Design of Sustainable Chemical Processes

2020 ◽  
pp. 435-449 ◽  
Author(s):  
Gonzalo Guillén-Gosálbez ◽  
Andrés González-Garay ◽  
Phantisa Limleamthong ◽  
Ángel Galán-Martín ◽  
Carlos Pozo
Keyword(s):  
Life Cycle ◽  
Chemical Processes ◽  
Life Cycle Optimization

Life-cycle optimization in the establishment of performance-acceptance parameters for seismic design

2002 ◽  
Vol 24 (2-4) ◽  
pp. 187-204 ◽  
Author(s):  
L. Esteva ◽  
O. Dı́az-López ◽  
J. Garcı́a-Pérez ◽  
G. Sierra ◽  
E. Ismael
Keyword(s):  
Life Cycle ◽  
Seismic Design ◽  
Life Cycle Optimization
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