Optimization of Smart Wells in the St. Joseph Field

2010 ◽  
Vol 13 (04) ◽  
pp. 588-595 ◽  
Author(s):  
G. M. van Essen ◽  
J. D. Jansen ◽  
D. R. Brouwer ◽  
S. G. Douma ◽  
M. J. Zandvliet ◽  
...  
Keyword(s):  
Optimization Technique ◽  
Gas Injection ◽  
Optimal Number ◽  
Control Parameters ◽  
Operational Strategies ◽  
Sector Model ◽  
Optimization Study ◽  
Smart Wells ◽  
Gradient Based ◽  
Reservoir Simulator

Summary The St. Joseph field has been on production since September 1981 under natural depletion supported by crestal gas injection. As part of a major redevelopment study, the scope for waterflooding was addressed using "smart" completions with multiple inflow control valves (ICVs) in the wells to be drilled for the redevelopment. Optimal control theory was used to optimize monetary value over the remaining producing life of the field, and in particular to select the optimal number of ICVs, the optimal configuration of the perforation zones, and the optimal operational strategies for the ICVs. A gradient-based optimization technique was implemented in a reservoir simulator equipped with the adjoint functionality to compute gradients of an objective function with respect to control parameters. For computational reasons, an initial optimization study was performed on a sector model, which showed promising results.

Finding the Best of the Best: Hydraulic Fracturing Design Optimization Study in Oman

2022 ◽  
Author(s):  
Rifat Kayumov ◽  
Ahmed Al Shueili ◽  
Musallam Jaboob ◽  
Hussain Al Salmi ◽  
Ricardo Sebastian Trejo ◽  
...  
Keyword(s):  
Hydraulic Fracturing ◽  
Pressure Gauge ◽  
Design Sensitivity ◽  
Actual Data ◽  
Earth Model ◽  
Reservoir Properties ◽  
Sector Model ◽  
Optimization Study ◽  
Exploration And Production ◽  
Reservoir Simulator

Abstract Development of the tight gas Khazzan Field in Sultanate of Oman has progressed through an extensive learning curve over many years. Thereby, the hydraulic fracturing design was fine-tuned and optimized to properly fit the requirements of the challenging Barik reservoir in this area. In 2018, BP Oman started developing the Barik reservoir in the Ghazeer Field, which naturally extends the reservoir boundary south of Khazzan Field. However, the Barik reservoir in the Ghazeer area is thicker and more permeable than in the Khazzan Field; therefore, the hydraulic fracturing design required adjustment to be optimized to directly reflect the reservoir needs of the Ghazeer Field. A comprehensive hydraulic fracturing design software was used for this optimization study and sensitivity analysis. This software is a plug-in to a benchmark exploration and production software platform and provides a complete fracturing optimization loop from hydraulic fracturing design sensitivity modelled with a calibrated mechanical earth model to detailed production prediction using the incorporated reservoir simulator. One of the stimulated wells from Ghazeer Field was used as the reference for this study. The reservoir sector model was created and adjusted to match actual data from this well. The data include fracturing treatment execution response, surveillance data such as radioactive tracers, bottomhole pressure gauge, and pressure transient analysis. Reservoir properties were also adjusted to match long-term production data obtained for this reference well. After the reservoir model was fully validated against actual data, multiple completion and fracturing scenarios were simulated to estimate potential production gain and thus find an optimal hydraulic fracturing design for Ghazeer Field. Many valuable outcomes can be concluded from this study. The optimal treatment design was identified. The value of fracture half-length versus conductivity was clarified for this area. The comparison between single-stage fracturing versus multistage treatment across the thick laminated Barik reservoir in a conventional vertical well was derived. The drainage of different layers with variable reservoir properties was compared for a range of different scenarios.

Field-Scale Modeling of Smart Completion Tools for Optimum Recovery

2021 ◽  
Author(s):  
Zhen Chen ◽  
Tareq Shaalan ◽  
Ghazi Qahtani ◽  
Shahid Manzoor
Keyword(s):  
Large Scale ◽  
Fractured Reservoirs ◽  
Naturally Fractured Reservoirs ◽  
Real Field ◽  
Full Field ◽  
Sector Model ◽  
Smart Wells ◽  
Control Devices ◽  
Reservoir Simulator ◽  
Complex Well

Abstract Flow control devices (FCDs) like inflow control devices (ICDs) and interval control valves (ICVs) (i.e., equalizer) have increased applications in both conventional and unconventional resources. They have been used to mitigate water or gas coning problems for mature fields in conventional reservoirs, to alleviate premature water breakthrough in naturally fractured reservoirs, and to optimize the steam distribution in heavy oil reservoirs. There have been increased trend in using FCDs in the real field. Previously, complex well models have been implemented in a large-scale parallel reservoir simulator by Tareq et al. (2017). The implementation can simulate an intelligent field contains tens to hundreds of multilateral complex wells commonly referred in the literature as maximum reservoir contact (MRC) wells with mechanical components such as ICVs and ICDs. In this paper, a new framework to model controlling the FCDs in complex well applications will be presented. The implementation is integrated into a complex well model. It can be easily used to model the dynamical control of devices. Simulation studies using both sector model and field model have been conducted. A systematic full-field operation is used for device control applications of smart wells. Successful application of field level controls in smart wells has the benefit of the improved overall GOSP performance.

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

scholarly journals A Hybrid Evolutionary Approach to Design Off-Grid Electrification Projects with Distributed Generation

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
J. Avilés ◽  
J. C. Mayo-Maldonado ◽  
O. Micheloud
Keyword(s):  
Distributed Generation ◽  
Network Topology ◽  
Optimization Technique ◽  
Optimal Number ◽  
Optimal Placement ◽  
Evolutionary Approach ◽  
Power Losses ◽  
Network Configuration ◽  
Optimal Network ◽  
Particle Swarm Optimization Technique

A hybrid evolutionary approach is proposed to design off-grid electrification projects that require distributed generation (DG). The design of this type of systems can be considered as an NP-Hard combinatorial optimization problem; therefore, due to its complexity, the approach tackles the problem from two fronts: optimal network configuration and optimal placement of DG. The hybrid scheme is based on a particle swarm optimization technique (PSO) and a genetic algorithm (GA) improved with a heuristic mutation operator. The GA-PSO scheme permits finding the optimal network topology, the optimal number, and capacity of the generation units, as well as their best location. Furthermore, the algorithm must design the system under power quality requirements, network radiality, and geographical constraints. The approach uses GPS coordinates as input data and develops a network topology from scratch, driven by overall costs and power losses minimization. Finally, the proposed algorithm is described in detail and real applications are discussed, from which satisfactory results were obtained.

Efficient Structural Design Using a Numerical Optimization Technique

2019 ◽  
Author(s):  
Qian Wang ◽  
Lucas Schmotzer ◽  
Yongwook Kim
Keyword(s):  
Structural Design ◽  
Numerical Optimization ◽  
Optimization Technique ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Convergence Criteria ◽  
Efficient Design ◽  
Concrete Building ◽  
Gradient Based ◽  
Structural Responses

<p>Structural designs of complex buildings and infrastructures have long been based on engineering experience and a trial-and-error approach. The structural performance is checked each time when a design is determined. An alternative strategy based on numerical optimization techniques can provide engineers an effective and efficient design approach. To achieve an optimal design, a finite element (FE) program is employed to calculate structural responses including forces and deformations. A gradient-based or gradient-free optimization method can be integrated with the FE program to guide the design iterations, until certain convergence criteria are met. Due to the iterative nature of the numerical optimization, a user programming is required to repeatedly access and modify input data and to collect output data of the FE program. In this study, an approximation method was developed so that the structural responses could be expressed as approximate functions, and that the accuracy of the functions could be adaptively improved. In the method, the FE program was not required to be directly looped in the optimization iterations. As a practical illustrative example, a 3D reinforced concrete building structure was optimized. The proposed method worked very well and optimal designs were found to reduce the torsional responses of the building.</p>

Gradient Based Optimization of the WAG Process with Smart Wells

2004 ◽  
Author(s):  
T.E.H. Esmaiel ◽  
S. Fallah ◽  
C.P.J.W. van Kruijsdijk
Keyword(s):  
Smart Wells ◽  
Gradient Based

scholarly journals Genetic Optimization-Based Consensus Control of Multi-Agent 6-DoF UAV System

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3576 ◽  
Author(s):  
Aws Najm ◽  
Ibraheem Ibraheem ◽  
Ahmad Azar ◽  
Amjad Humaidi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Technique ◽  
Switching Topology ◽  
Control Law ◽  
Genetic Optimization ◽  
Multi Agent System ◽  
Control Parameters ◽  
Consensus Control ◽  
Communication Topology ◽  
Multi Agent

A consensus control law is proposed for a multi-agent system of quadrotors with leader–follower communication topology for three quadrotor agents. The genetic algorithm (GA) is the proposed optimization technique to tune the consensus control parameters. The complete nonlinear model is used without any further simplifications in the simulations, while simplification in the model is used to theoretically design the controller. Different case studies and tests are done (i.e., trajectory tracking formation and switching topology) to show the effectiveness of the proposed controller. The results show good performance in all tests while achieving the consensus of the desired formations.

Global Optimization of Gas Allocation to a Group of Wells in Artificial Lift Using MATLAB

2001 ◽  
Author(s):  
Gabriel A. Alarcón ◽  
Carlos F. Torres-Monzón ◽  
Nellyana Gonzalo ◽  
Luis E. Gómez
Keyword(s):  
Production Rate ◽  
Optimization Technique ◽  
Gas Injection ◽  
Nonlinear Function ◽  
Oil Production ◽  
Oil Well ◽  
Artificial Lift ◽  
Total Oil ◽  
Gas Lift ◽  
Injection Rates

Abstract Continuous flow gas lift is one of the most common artificial lift method in the oil industry and is widely used in the world. A continuous volume of gas is injected at high pressure into the bottom of the tubing, to gasify the oil column and thus facilitate the extraction. If there is no restriction in the amount of injection gas available, sufficient gas can be injected into each oil well to reach maximum production. However, the injection gas available is generally insufficient. An inefficient gas allocation in a field with limited gas supply also reduces the revenues, since excessive gas injection is expensive due to the high gas prices and compressing costs. Therefore, it is necessary to assign the injection gas into each well in optimal form to obtain the field maximum oil production rate. The gas allocation optimization can be considered as a maximization of a nonlinear function, which models the total oil production rate for a group of wells. The variables or unknowns for this function are the gas injection rates for each well, which are subject to physical restrictions. In this work a MATLAB™ nonlinear optimization technique with constraints was implemented to find the optimal gas injection rates. A new mathematical fit to the “Gas-Lift Performance Curve” is presented and the numeric results of the optimization are given and compared with results of other methods published in the specialized literature. The optimization technique proved fast convergence and broad application.

scholarly journals Compatibility of objective functions with simplex algorithm for controller tuning of HVDC system

2020 ◽  
Vol 39 (3) ◽  
pp. 34-43
Author(s):  
Haaris Rasool ◽  
Aazim Rasool ◽  
Ataul Aziz Ikram ◽  
Urfa Rasool ◽  
Mohsin Jamil ◽  
...  
Keyword(s):  
Optimization Technique ◽  
Absolute Error ◽  
Simplex Algorithm ◽  
Control Parameters ◽  
Objective Functions ◽  
Simulation Design ◽  
Hvdc System ◽  
Integral Time ◽  
Time Simulation ◽  
Simulation Based

This work aims to tune multiple controllers at the same time for a HVDC system by using a self-generated (SG) simulation-based optimization technique. Online optimization is a powerful tool to improve performance of the system. Proportion integral (PI) controllers of Multi-infeed HVDC systems are optimized by the evaluation of objective functions in time simulation design (TSD). Model based simulation setup is applied for rapid selection of optimal PI control parameters, designed in PSCAD software. A multiple objective function (OF), i.e. Integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE), integral time square error (ITSE), and integral square time error (ISTE), is assembled for testing the compatibility of OFs with nonlinear self-generated simplex algorithm (SS-SA). Improved control parameters are achieved after multiple iterations. All OFs generate optimum responses and their results are compared with each other by their minimized numerical values. Disturbance rejection criteria are also proposed to assess the designed controller performance along with robustness of system. Results are displayed in form of graphs and tables in this paper.

Coupling a Reservoir Simulator With a Network Model to Evaluate the Implementation of Smart Wells on the Moporo Field in Venezuela

2009 ◽  
Author(s):  
Argenis Jesus Alvarez ◽  
Edilena Guerra ◽  
Alexis Gammiero ◽  
Cesar Velasquez ◽  
Jose Perdomo ◽  
...  
Keyword(s):  
Network Model ◽  
Smart Wells ◽  
Reservoir Simulator
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