Drilling-Campaign Optimization Using Sequential Information and Policy Analytics

SPE Journal ◽  
2021 ◽  
pp. 1-17
Author(s):  
André Luís Morosov ◽  
Reidar Brumer Bratvold
Keyword(s):  
Oil Recovery ◽  
Information Acquisition ◽  
Optimal Number ◽  
Optimization Approach ◽  
Complex Data ◽  
Sequential Decision ◽  
Geological Uncertainty ◽  
First Case ◽  
Geological Information ◽  
Policy Analytics

Summary Optimally designed drilling campaigns are essential for improving oil recovery and value creation. They are required at different stages of the hydrocarbon-field life cycle, including exploration, appraisal, development, and infill. A significant fraction of the revenue risk comes from geological uncertainty, and for this reason, subsurface teams are frequently responsible for optimizing campaign parameters such as the number of wells, the corresponding locations, and the drilling sequence. Companies use the information and learning from drilled wells to adapt the remainder of the campaign, but classical optimization methods do not account for such learning and flexibility over time. Accounting for sequential geological information acquisition and decision making in the optimization of drilling campaigns adds value to the project. We propose a method to optimize drilling campaigns under geological uncertainty by using a sequential-decision model to obtain the optimal drilling policy and applying analytics over the policy to obtain the optimal number of wells and corresponding locations. The novel contribution of policy analytics provides better access to information within the complex data structure of the optimal policy, providing decision support for different decision criteria. The method is demonstrated in two different cases. The first case considers a set of eight candidate wells on predefined locations, mimicking the situation where the method is used after a prior subsurface optimization. The second case considers a set of 12 candidate wells regularly scattered in the same area and uses the method as the first optimization approach to filter out less-attractive regions. Exploiting the geological information on a well-by-well basis improved the expected campaign value by 65% in the first case and by 183% in the second case. The value of spatial geological information and value of flexibility from having more drilling candidates are two byproducts of the method application.

Superstructure–foundation interaction in multi-objective pile group optimization considering settlement response

2017 ◽  
Vol 54 (10) ◽  
pp. 1408-1420 ◽  
Author(s):  
Y.F. Leung ◽  
A. Klar ◽  
K. Soga ◽  
N.A. Hoult
Keyword(s):  
Shear Walls ◽  
Field Measurements ◽  
Pile Group ◽  
Optimization Approach ◽  
Full Potential ◽  
Foundation Design ◽  
Soft Storey ◽  
Multi Objective ◽  
Piled Raft ◽  
First Case

The full potential of pile optimization has not been realized as the interactions between superstructures and foundations, and the relationships between material usage and foundation performance are rarely investigated. This paper introduces an analysis and optimization approach for pile group and piled raft foundations, which allows coupling of superstructure stiffness with the foundation model, through a condensed matrix representing the flexural characteristics of the superstructure. This coupled approach is implemented within a multi-objective optimization algorithm, capable of providing a series of optimized pile configurations at various amounts of material. The approach is illustrated through two case studies. The first case involves evaluation of the coupled superstructure–foundation analyses against field measurements of a piled raft–supported building in London, UK. The potential benefits of pile optimization are also demonstrated through re-analyses of the foundation by the proposed optimization approach. In the second case, the effects of a soft storey on the superstructure–foundation interactions are investigated. These cases demonstrate the importance of properly considering the superstructure effects, especially when the building consists of stiff components such as concrete shear walls. The proposed approach also allows engineers to make informed decisions on the foundation design, depending on the specific project finances and performance requirements.

Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study

2021 ◽  
Author(s):  
Federico Peralta Samaniego ◽  
Sergio Toral Marín ◽  
Daniel Gutierrez Reina
Keyword(s):  
Water Quality ◽  
Mean Squared Error ◽  
Bayesian Optimization ◽  
Water Quality Parameter ◽  
Optimization Approach ◽  
Decision Strategy ◽  
Sequential Decision ◽  
Partition System ◽  
Autonomous Surface Vehicle ◽  
Voronoi Partition

<div>Bayesian optimization is a popular sequential decision strategy that can be used for environmental monitoring. In this work, we propose an efficient multi-Autonomous Surface Vehicle system capable of monitoring the Ypacarai Lake (San Bernardino, Paraguay) (60 km<sup>2</sup>) using the Bayesian optimization approach with a Voronoi Partition system. The system manages to quickly approximate the real unknown distribution map of a water quality parameter using Gaussian Processes as surrogate models. Furthermore, to select new water quality measurement locations, an acquisition function adapted to vehicle energy constraints is used. Moreover, a Voronoi Partition system helps to distributing the workload with all the available vehicles, so that robustness and scalability is assured. For evaluation purposes, we use both the mean squared error and computational efficiency. The results showed that our method manages to efficiently monitor the Ypacarai Lake, and also provides confident approximate models of water quality parameters. It has been observed that, for every vehicle, the resulting surrogate model improves by 38%.</div>

scholarly journals Modelling Low-Salinity Water Flooding as a Tertiary Oil Recovery Technique

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Oluwasanmi Olabode ◽  
David Alaigba ◽  
Daniel Oramabo ◽  
Oreofeoluwa Bamigboye
Keyword(s):  
Oil Recovery ◽  
Water Flooding ◽  
High Salinity Water ◽  
Low Salinity ◽  
Low Salinity Water ◽  
First Case ◽  
Short Period ◽  
Tertiary Oil Recovery ◽  
Salinity Water ◽  
Field Production

In this project, low-salinity water flooding has been modeled on ECLIPSE black oil simulator in three cases for a total field production life of twenty-five years. In the first case, low-salinity water flooding starts fifteen years after secondary water flooding. For the second case, low-salinity water flooding starts five years after secondary water flooding and runs till the end of the field production life. For the third case, low-salinity water flooding starts five years after secondary water flooding, but low-salinity water flooding is injected in measured pore volumes for a short period of time; then, high-salinity water flooding was resumed till the end of the field production life. This was done to measure the effect of low-salinity water flooding as slug injection. From the three cases presented, oil recovery efficiency, field oil production rate, and field water cut were observed. Increased percentages of 22.66%, 35.12%, and 26.77% were observed in the three cases, respectively.

scholarly journals State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1546
Author(s):  
M. S. Hossain Lipu ◽  
M. A. Hannan ◽  
Aini Hussain ◽  
Afida Ayob ◽  
Mohamad H. M. Saad ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Lithium Ion Batteries ◽  
Lithium Ion ◽  
Optimal Number ◽  
State Of Charge ◽  
Optimization Approach ◽  
Safe Driving ◽  
Soc Estimation ◽  
Improved Firefly Algorithm

The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences.

Parametric Study on Economic Feasibility of Microturbine Cogeneration Systems by an Optimization Approach

2005 ◽  
Vol 127 (2) ◽  
pp. 389-396 ◽  
Author(s):  
Satoshi Gamou ◽  
Ryohei Yokoyama ◽  
Koichi Ito
Keyword(s):  
Energy Demand ◽  
Unit Cost ◽  
Maximum Energy ◽  
Optimal Number ◽  
Economic Feasibility ◽  
Optimization Approach ◽  
Energy Demands ◽  
Equation Problem ◽  
Numerical Studies ◽  
Cogeneration System

Economic feasibility of microturbine cogeneration systems is investigated by analyzing relationships between the optimal number of microturbine units and the maximum energy demands under various conditions. For this purpose, a method to obtain the maximum energy demand at which the optimal number changes is proposed by combining a nonlinear equation problem and an optimal unit sizing problem hierarchically. Based on the proposed method, a map expressing the aforementioned relationships can be illustrated. Through numerical studies carried out on systems installed in hotels by changing the electrical generating efficiency and the capital unit cost of the microturbine cogeneration unit as parameters, the influence of the parameters on the economic feasibility of the microturbine cogeneration system is clarified.

scholarly journals Bridge Management Strategy Based on Extreme User Costs for Bridge Network Condition

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Ladislaus Lwambuka ◽  
Primus V. Mtenga
Keyword(s):  
Multiobjective Optimization ◽  
Traffic Flow ◽  
Developed Countries ◽  
Optimization Approach ◽  
User Cost ◽  
Bridge Management ◽  
User Costs ◽  
First Case ◽  
Conflicting Objectives ◽  
Alternative Routes

This paper presents a practical approach for prioritization of bridge maintenance within a given bridge network. The maintenance prioritization is formulated as a multiobjective optimization problem where the simultaneous satisfaction of several conflicting objectives includes minimization of maintenance costs, maximization of bridge deck condition, and minimization of traffic disruption and associated user costs. The prevalence of user cost during maintenance period is twofold; the first case refers to the period of dry season where normally the traffic flow is diverted to alternative routes usually resurfaced to regain traffic access. The second prevalence refers to the absence of alternative routes which is often the case in the least developed countries; in this case the user cost referred to results from the waiting time while the traffic flow is put on hold awaiting accomplishment of the maintenance activity. This paper deals with the second scenario of traffic closure in the absence of alternative diversion routes which in essence results in extreme user cost. The paper shows that the multiobjective optimization approach remains valid for extreme cases of user costs in the absence of detour roads as often is the scenario in countries with extreme poor road infrastructure.

Optimal Well Placement Under Uncertainty Using a Retrospective Optimization Framework

SPE Journal ◽  
2011 ◽  
Vol 17 (01) ◽  
pp. 112-121 ◽  
Author(s):  
Honggang Wang ◽  
David Echeverría Ciaurri ◽  
Louis J. Durlofsky ◽  
Alberto Cominelli
Keyword(s):  
Optimization Algorithm ◽  
Net Present Value ◽  
Linear Interpolation ◽  
Cluster Sampling ◽  
Optimization Approach ◽  
Geological Uncertainty ◽  
Direct Optimization ◽  
Reservoir Performance ◽  
Subsurface Geology ◽  
Order Of Magnitude

Summary Subsurface geology is highly uncertain, and it is necessary to account for this uncertainty when optimizing the location of new wells. This can be accomplished by evaluating reservoir performance for a particular well configuration over multiple realizations of the reservoir and then optimizing based, for example, on expected net present value (NPV) or expected cumulative oil production. A direct procedure for such an optimization would entail the simulation of all realizations at each iteration of the optimization algorithm. This could be prohibitively expensive when it is necessary to use a large number of realizations to capture geological uncertainty. In this work, we apply a procedure that is new within the context of reservoir management—retrospective optimization (RO)—to address this problem. RO solves a sequence of optimization subproblems that contain increasing numbers of realizations. We introduce the use of k -means clustering for selecting these realizations. Three example cases are presented that demonstrate the performance of the RO procedure. These examples use particle swarm optimization (PSO) and simplex linear interpolation (SLI)-based line search as the core optimizers (the RO framework can be used with any underlying optimization algorithm, either stochastic or deterministic). In the first example, we achieve essentially the same optimum using RO as we do using a direct optimization approach, but RO requires an order of magnitude fewer simulations. The results demonstrate the advantages of cluster-based sampling over random sampling for the examples considered. Taken in total, our findings indicate that RO using cluster sampling represents a promising approach for optimizing well locations under geological uncertainty.

scholarly journals COVID-19 ResNet: Residual neural network for COVID-19 classification with bayesian data augmentation

2021 ◽  
Vol 13 (2) ◽  
pp. 19
Author(s):  
Maria Baldeon calisto ◽  
Javier Sebastián Balseca Zurita ◽  
Martin Alejandro Cruz Patiño
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Data Augmentation ◽  
Optimization Approach ◽  
Hyperparameter Optimization ◽  
First Case ◽  
Augmentation Strategy ◽  
Medical Image Classification ◽  
Novel Coronavirus ◽  
Insight Into

COVID-19 is an infectious disease caused by a novel coronavirus called SARS-CoV-2. The first case appeared in December 2019, and until now it still represents a significant challenge to many countries in the world. Accurately detecting positive COVID-19 patients is a crucial step to reduce the spread of the disease, which is characterize by a strong transmission capacity. In this work we implement a Residual Convolutional Neural Network (ResNet) for an automated COVID-19 diagnosis. The implemented ResNet can classify a patient´s Chest-Xray image into COVID-19 positive, pneumonia caused from another virus or bacteria, and healthy. Moreover, to increase the accuracy of the model and overcome the data scarcity of COVID-19 images, a personalized data augmentation strategy using a three-step Bayesian hyperparameter optimization approach is applied to enrich the dataset during the training process. The proposed COVID-19 ResNet achieves a 94% accuracy, 95% recall, and 95% F1-score in test set. Furthermore, we also provide an insight into which data augmentation operations are successful in increasing a CNNs performance when doing medical image classification with COVID-19 CXR.

scholarly journals Analyzing the performance of variational quantum factoring on a superconducting quantum processor

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Amir H. Karamlou ◽  
William A. Simon ◽  
Amara Katabarwa ◽  
Travis L. Scholten ◽  
Borja Peropadre ◽  
...  
Keyword(s):  
Success Probability ◽  
Optimal Number ◽  
Optimization Approach ◽  
Noise Sources ◽  
Quantum Processor ◽  
Circuit Depth ◽  
Near Term ◽  
Quantum Optimization ◽  
The Impact ◽  
Source Of Error

AbstractIn the near-term, hybrid quantum-classical algorithms hold great potential for outperforming classical approaches. Understanding how these two computing paradigms work in tandem is critical for identifying areas where such hybrid algorithms could provide a quantum advantage. In this work, we study a QAOA-based quantum optimization approach by implementing the Variational Quantum Factoring (VQF) algorithm. We execute experimental demonstrations using a superconducting quantum processor, and investigate the trade off between quantum resources (number of qubits and circuit depth) and the probability that a given biprime is successfully factored. In our experiments, the integers 1099551473989, 3127, and 6557 are factored with 3, 4, and 5 qubits, respectively, using a QAOA ansatz with up to 8 layers and we are able to identify the optimal number of circuit layers for a given instance to maximize success probability. Furthermore, we demonstrate the impact of different noise sources on the performance of QAOA, and reveal the coherent error caused by the residual ZZ-coupling between qubits as a dominant source of error in a near-term superconducting quantum processor.

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