scholarly journals Petroleum Reservoir Control Optimization with the Use of the Auto-Adaptive Decision Trees

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5702
Author(s):  
Edyta Kuk ◽  
Jerzy Stopa ◽  
Michał Kuk ◽  
Damian Janiga ◽  
Paweł Wojnarowski
Keyword(s):  
Decision Tree ◽  
Energy Demand ◽  
Petroleum Reservoir ◽  
Control Sequence ◽  
Limit Values ◽  
Control Optimization ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
Global Increase ◽  
Reservoir Simulator

The global increase in energy demand and the decreasing number of newly discovered hydrocarbon reservoirs caused by the relatively low oil price means that it is crucial to exploit existing reservoirs as efficiently as possible. Optimization of the reservoir control may increase the technical and economic efficiency of the production. In this paper, a novel algorithm that automatically determines the intelligent control maximizing the NPV of a given production process was developed. The idea is to build an auto-adaptive parameterized decision tree that replaces the arbitrarily selected limit values for the selected attributes of the decision tree with parameters. To select the optimal values of the decision tree parameters, an AI-based optimization tool called SMAC (Sequential Model-based Algorithm Configuration) was used. In each iteration, the generated control sequence is introduced into the reservoir simulator to compute the NVP, which is then utilized by the SMAC tool to vary the limit values to generate a better control sequence, which leads to an improved NPV. A new tool connecting the parameterized decision tree with the reservoir simulator and the optimization tool was developed. Its application on a simulation model of a real reservoir for which the CCS-EOR process was considered allowed oil production to be increased by 3.5% during the CO2-EOR phase, reducing the amount of carbon dioxide injected at that time by 16%. Hence, the created tool allowed revenue to be increased by 49%.

Innovative Volumetric Solar Receiver Micro-Design Based on Numerical Predictions

2015 ◽  
Author(s):  
Raffaele Capuano ◽  
Thomas Fend ◽  
Bernhard Hoffschmidt ◽  
Robert Pitz-Paal
Keyword(s):  
Solar Radiation ◽  
Energy Demand ◽  
Large Scale ◽  
Solar Power ◽  
Numerical Models ◽  
Numerical Predictions ◽  
Proper Design ◽  
Global Increase ◽  
Solar Power Tower ◽  
Solar Receiver

Due to the continuous global increase in energy demand, Concentrated Solar Power (CSP) represents an excellent alternative, or add-on to existing systems for the production of energy on a large scale. In some of these systems, the Solar Power Tower plants (SPT), the conversion of solar radiation into heat occurs in certain components defined as solar receivers, placed in correspondence of the focus of the reflected sunlight. In a particular type of solar receivers, defined as volumetric, the use of porous materials is foreseen. These receivers are characterized by a porous structure called absorber. The latter, hit by the reflected solar radiation, transfers the heat to the evolving fluid, generally air subject to natural convection. The proper design of these elements is essential in order to achieve high efficiencies, making such structures extremely beneficial for the overall performances of the energy production process. In the following study, a parametric analysis and an optimized characterization of the structure have been performed with the use of self-developed numerical models. The knowledge and results gained through this study have been used to define an optimization path in order to improve the absorber microstructure, starting from the current in-house state-of-the-art technology until obtaining a new advanced geometry.

scholarly journals Smart Optimization of Proactive Control of Petroleum Reservoir

2021 ◽  
Vol 7 (1) ◽  
pp. 304-313
Author(s):  
Edyta Kuk ◽  
Michał Kuk ◽  
Damian Janiga ◽  
Paweł Wojnarowski ◽  
Jerzy Stopa
Keyword(s):  
Artificial Intelligence ◽  
Optimal Control ◽  
Decision Tree ◽  
Clustering Algorithm ◽  
Petroleum Reservoirs ◽  
Economic Benefits ◽  
Petroleum Reservoir ◽  
Cluster Assignment ◽  
Economic Point ◽  
Temporal Clustering

Artificial Intelligence plays an increasingly important role in many industrial applications as it has great potential for solving complex engineering problems. One of such applications is the optimization of petroleum reservoirs production. It is crucial to produce hydrocarbons efficiently as their geological resources are limited. From an economic point of view, optimization of hydrocarbon well control is an important factor as it affects the whole market. The solution proposed in this paper is based on state-of-the-art artificial intelligence methods, optimal control, and decision tree theory. The proposed idea is to apply a novel temporal clustering algorithm utilizing an autoencoder for temporal dimensionality reduction and a temporal clustering layer for cluster assignment, to cluster wells into groups depending on the production situation that occurs in the vicinity of the well, which allows reacting proactively. Then the optimal control of wells belonging to specific groups is determined using an auto-adaptive decision tree whose parameters are optimized using a novel sequential model-based algorithm configuration method. Optimization of petroleum reservoirs production translates directly into several economic benefits: reduction in operation costs, increase in the production effectiveness and increase in overall income without any extra expenditure as only control is changed. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Output-Constraint Handling and Parallelization for Oil-Reservoir Control Optimization by Means of Multiple Shooting

SPE Journal ◽  
2015 ◽  
Vol 20 (04) ◽  
pp. 856-871 ◽  
Author(s):  
Andrés Codas ◽  
Bjarne Foss ◽  
Eduardo Camponogara
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problem ◽  
Computation Time ◽  
Building Blocks ◽  
Programming Algorithm ◽  
Multiple Shooting ◽  
State Variables ◽  
Control Optimization ◽  
Output Constraints ◽  
Reservoir Simulator

Summary We propose to formulate and solve the reservoir-control optimization problem with the direct multiple-shooting method. This method divides the optimal-control problem prediction horizon in smaller intervals that one can evaluate in parallel. Further, output constraints are easily established on each interval boundary and as such hardly affect computation time. This opens new opportunities to include state constraints on a much broader scale than is common in reservoir optimization today. However, multiple shooting deals with a large number of variables because it decides on the boundary-state variables of each interval. Therefore, we exploit the structure of the reservoir simulator to conceive a variable-reduction technique to solve the optimization problem with a reduced sequential quadratic-programming algorithm. We discuss the optimization-algorithm building blocks and focus on structure exploitation and parallelization opportunities. To demonstrate the method's capabilities to handle output constraints, the optimization algorithm is interfaced to an open-source reservoir simulator. Then, on the basis of a widely used reservoir model, we evaluate performance, especially related to output constraints. The performance of the proposed method is qualitatively compared with a conventional method.

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector

2015 ◽  
Vol 53 ◽  
pp. 745-778 ◽  
Author(s):  
Marius Lindauer ◽  
Holger H. Hoos ◽  
Frank Hutter ◽  
Torsten Schaub
Keyword(s):  
State Of The Art ◽  
Machine Learning Techniques ◽  
Problem Instance ◽  
Algorithm Selection ◽  
The Core ◽  
Automated Algorithm ◽  
Learning Techniques ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. This holds specifically for the machine learning techniques that form the core of current AS procedures, and for their hyperparameters. Therefore, to successfully apply AS to new problems, algorithms and benchmark sets, two questions need to be answered: (i) how to select an AS approach and (ii) how to set its parameters effectively. We address both of these problems simultaneously by using automated algorithm configuration. Specifically, we demonstrate that we can automatically configure claspfolio 2, which implements a large variety of different AS approaches and their respective parameters in a single, highly-parameterized algorithm framework. Our approach, dubbed AutoFolio, allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods. We demonstrate AutoFolio can significantly improve the performance of claspfolio 2 on 8 out of the 13 scenarios from the Algorithm Selection Library, leads to new state-of-the-art algorithm selectors for 7 of these scenarios, and matches state-of-the-art performance (statistically) on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieves average speedup factors between 1.3 and 15.4.

scholarly journals AutoFolio: An Automatically Configured Algorithm Selector (Extended Abstract)

2017 ◽  
Author(s):  
Marius Lindauer ◽  
Frank Hutter ◽  
Holger H. Hoos ◽  
Torsten Schaub
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
The State ◽  
Problem Instance ◽  
Algorithm Selection ◽  
Algorithm Configuration ◽  
Optimal Values ◽  
Art Performance ◽  
The One

Algorithm selection (AS) techniques -- which involve choosing from a set of algorithms the one expected to solve a given problem instance most efficiently -- have substantially improved the state of the art in solving many prominent AI problems, such as SAT, CSP, ASP, MAXSAT and QBF. Although several AS procedures have been introduced, not too surprisingly, none of them dominates all others across all AS scenarios. Furthermore, these procedures have parameters whose optimal values vary across AS scenarios. In this extended abstract of our 2015 JAIR article of the same title, we summarize AutoFolio, which uses an algorithm configuration procedure to automatically select an AS approach and optimize its parameters for a given AS scenario. AutoFolio allows researchers and practitioners across a broad range of applications to exploit the combined power of many different AS methods and to automatically construct high-performance algorithm selectors. We demonstrate that AutoFolio was able to produce new state-of-the-art algorithm selectors for 7 well-studied AS scenarios and matches state-of-the-art performance statistically on all other scenarios. Compared to the best single algorithm for each AS scenario, AutoFolio achieved average speedup factors between 1.3 and 15.4.

scholarly journals A decision tree method for building energy demand modeling

2010 ◽  
Vol 42 (10) ◽  
pp. 1637-1646 ◽  
Author(s):  
Zhun Yu ◽  
Fariborz Haghighat ◽  
Benjamin C.M. Fung ◽  
Hiroshi Yoshino
Keyword(s):  
Decision Tree ◽  
Energy Demand ◽  
Building Energy ◽  
Demand Modeling ◽  
Decision Tree Method ◽  
Tree Method

Toward Cost-Effective Reservoir Simulation Solvers on GPUs

2016 ◽  
Vol 8 (6) ◽  
pp. 971-991
Author(s):  
Zheng Li ◽  
Shuhong Wu ◽  
Jinchao Xu ◽  
Chensong Zhang
Keyword(s):  
Reservoir Simulation ◽  
Cost Effective ◽  
Fine Tuning ◽  
Processing Unit ◽  
Petroleum Reservoir ◽  
Fully Implicit ◽  
Hardware Architectures ◽  
Graphical Processing ◽  
Reservoir Simulator ◽  
Many Core

AbstractIn this paper, we focus on graphical processing unit (GPU) and discuss how its architecture affects the choice of algorithm and implementation of fully-implicit petroleum reservoir simulation. In order to obtain satisfactory performance on new many-core architectures such as GPUs, the simulator developers must know a great deal on the specific hardware and spend a lot of time on fine tuning the code. Porting a large petroleum reservoir simulator to emerging hardware architectures is expensive and risky. We analyze major components of an in-house reservoir simulator and investigate how to port them to GPUs in a cost-effective way. Preliminary numerical experiments show that our GPU-based simulator is robust and effective. More importantly, these numerical results clearly identify the main bottlenecks to obtain ideal speedup on GPUs and possibly other many-core architectures.

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