Nonlinear risk optimization approach to water drive gas reservoir production optimization using DOE and artificial intelligence

2016 ◽  
Vol 31 ◽  
pp. 575-584 ◽  
Author(s):  
Meysam Naderi ◽  
Ehsan Khamehchi
Keyword(s):  
Artificial Intelligence ◽  
Production Optimization ◽  
Optimization Approach ◽  
Gas Reservoir ◽  
Risk Optimization

Digital Solutions Suite: Big Data, Artificial Intelligence, and Digital Barrel

2021 ◽  
Author(s):  
Roberto Carlos Fuenmayor
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Process Management ◽  
Production Optimization ◽  
Multiple Sources ◽  
Multiple Systems ◽  
New Ways Of Working ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Engineering Models

Abstract The concept of digital transformation is based on two principles: data driven—exploiting every bit of data source—and user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract insights that are the product of the aggregation of multiple sources then present it to the user (field manager, production and surveillance engineer, region manager, and country) with criteria's of simplicity, specificity, novelty—and most importantly, clarity. The idea is to liberate the data across the whole upstream community and intended for production operations people by providing a one-stop production digital platform that taps into unstructured data and is transformed into structured to be used as input to engineering models and as a result provide data analytics and generate insights. There is three main key objectives: To have only one source of truth using cloud-based technology To incorporate artificial intelligence models to fill the data gaps of production and operations parameters such as pressure and temperature To incorporate multiple solutions for the upstream community that helps during the slow, medium, and fast loops of upstream operations. The new "way of working" helps multiple disciplines such as subsurface team, facilities, and operations, HSSE and business planning, combining business process management and technical workflows to generates insights and create value that impact the profit and losses (P&L) sheet of the operators. The "new ways of working" tackle values pillars such as production optimization, reduced unplanned deferment, cost avoidance, and improved process cycle efficiency. The use of big data and artificial intelligence algorithms are key to understand the production of the wells and fields, as well as anchoring on processing the data with automated engineering models, thus enabling better decision making including the span of time scale such as fast, medium, or slow loop actions.

scholarly journals Dynamic and Optimized Prototype Clustering for Relational Data based on Multiple Prototypes

2019 ◽  
Vol 8 (3) ◽  
pp. 5630-5634
Keyword(s):  
Artificial Intelligence ◽  
Data Clustering ◽  
Synthetic Data ◽  
Medical Data ◽  
Relational Data ◽  
Data Sets ◽  
Optimization Approach ◽  
Complex Task ◽  
Data Points ◽  
Clustering Approach

In artificial intelligence related applications such as bio-medical, bio-informatics, data clustering is an important and complex task with different situations. Prototype based clustering is the reasonable and simplicity to describe and evaluate data which can be treated as non-vertical representation of relational data. Because of Barycentric space present in prototype clustering, maintain and update the structure of the cluster with different data points is still challenging task for different data points in bio-medical relational data. So that in this paper we propose and introduce A Novel Optimized Evidential C-Medoids (NOEC) which is relates to family o prototype based clustering approach for update and proximity of medical relational data. We use Ant Colony Optimization approach to enable the services of similarity with different features for relational update cluster medical data. Perform our approach on different bio-medical related synthetic data sets. Experimental results of proposed approach give better and efficient results with comparison of different parameters in terms of accuracy and time with processing of medical relational data sets.

scholarly journals Short-Term Model-Based Production Optimization for a Gas Field in North Africa

2018 ◽  
Vol 10 (2) ◽  
pp. 65
Author(s):  
Arnaud Hoffmann
Keyword(s):  
Piecewise Linear ◽  
Treatment Plant ◽  
Gas Production ◽  
Production Optimization ◽  
Mixed Integer ◽  
Short Term ◽  
Gas Reservoir ◽  
Gas Field ◽  
Gas Treatment ◽  
Model Based

 This paper presents a model-based optimization solution suitable for short-term production optimization of large gas fields with wells producing into a common surface network into a shared gas treatment plant. The proposed methodology is applied to a field consisting of one dry gas reservoir with a CO2 content of 7.3% and one wet gas reservoir with a CO2 content of 2.8% and initial CGR of 15 stb/MMscf. 23 wells are producing, and all gas production is processed in a common gas treatment plant where condensates and CO2 are extracted from the reservoir gas. The final sales gas must honor compositional constraints (CO2 content and heating value). The proposed solution consists of a bi-level optimization algorithm. A Mixed Integer Linear Programming (MILP) formulation of the optimization problem is solved, assuming some key parameters in the gas plant to be constant. Hydraulic performances of the system, approximated using SOS2 piecewise linear models, and condensates and CO2 extraction, captured using simplified models, are included in the MILP. After solving the MILP, the values of the key parameters are calculated using a full simulation model of the gas plant and the new values are substituted in the MILP input data. This iterative procedure continues until convergence is achieved. Results show that the proposed methodology can find the optimum choke openings for all wells to maximize the total gas rate while honoring numerous surface constraints. The solution runs in 30 sec. and an average of 3-4 iterations is needed to achieve convergence. It is therefore a suitable solution for short-term production optimization and daily operations.

scholarly journals A Workflow for Integrated Geological Modeling for Shale Gas Reservoirs: A Case Study of the Fuling Shale Gas Reservoir in the Sichuan Basin, China

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Xiaofei Shang ◽  
Huawei Zhao ◽  
Shengxiang Long ◽  
Taizhong Duan
Keyword(s):  
Shale Gas ◽  
Production Optimization ◽  
Natural Fracture ◽  
Geological Modeling ◽  
Gas Reservoirs ◽  
Gas Reservoir ◽  
Shale Gas Reservoir ◽  
Shale Gas Reservoirs ◽  
Stratigraphic Model

Shale gas reservoir evaluation and production optimization both require geological models. However, currently, shale gas modeling remains relatively conventional and does not reflect the unique characteristics of shale gas reservoirs. Based on a case study of the Fuling shale gas reservoir in China, an integrated geological modeling workflow for shale gas reservoirs is proposed to facilitate its popularization and application and well improved quality and comparability. This workflow involves four types of models: a structure-stratigraphic model, reservoir (matrix) parameter model, natural fracture (NF) model, and hydraulic fracture (HF) model. The modeling strategies used for the four types of models vary due to the uniqueness of shale gas reservoirs. A horizontal-well lithofacies sublayer calibration-based method is employed to build the structure-stratigraphic model. The key to building the reservoir parameter model lies in the joint characterization of shale gas “sweet spots.” The NF models are built at various scales using various methods. Based on the NF models, the HF models are built by extended simulation and microseismic inversion. In the entire workflow, various types of models are built in a certain sequence and mutually constrain one another. In addition, the workflow contains and effectively integrates multisource data. Moreover, the workflow involves multiple model integration processes, which is the key to model quality. The selection and optimization of modeling methods, the innovation and development of modeling algorithms, and the evaluation techniques for model uncertainty are areas where breakthroughs may be possible in the geological modeling of shale gas reservoirs. The workflow allows the complex process of geological modeling of shale gas reservoirs to be more systematic. It is of great significance for a dynamic analysis of reservoir development, from individual wells to the entire gas field, and for optimizing both development schemes and production systems.

scholarly journals Improving the Dipping Step in Czochraski Process Using Haar-Cascade Algorithm

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 646
Author(s):  
Le Tran Huu Phuc ◽  
HyeJun Jeon ◽  
Nguyen Tam Nguyen Truong ◽  
Jung Jae Hak
Keyword(s):  
Artificial Intelligence ◽  
Crystal Growth ◽  
Labor Cost ◽  
High Accuracy ◽  
Optimization Approach ◽  
Cascade Algorithm ◽  
Charge Coupled Device ◽  
Czochralski Crystal Growth ◽  
Haar Cascade ◽  
A Charge

Czochralski crystal growth has become a popular technique to produce pure single crystals. Many methods have also been developed to optimize this process. In this study, a charge-coupled device camera was used to record the crystal growth progress from beginning to end. The device outputs images which were then used to create a classifier using the Haar-cascade and AdaBoost algorithms. After the classifier was generated, artificial intelligence (AI) was used to recognize the images obtained from good dipping and calculate the duration of this operating. This optimization approach improved a Czochralski which can detect a good dipping step automatically and measure the duration with high accuracy. Using this development, the labor cost of the Czochralski system can be reduced by changing the contribution of human specialists’ mission.

scholarly journals A New Approach for Optimizing Management of a Real Time Solar Charger Using the Firebase Platform Under Android

2019 ◽  
Vol 9 (3) ◽  
pp. 23
Author(s):  
Mohamed Redha Rezoug ◽  
Rachid Chenni ◽  
Djamel Taibi
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Production Optimization ◽  
Optimization Approach ◽  
Continuous Growth ◽  
New Approach ◽  
Charging Time ◽  
Storage Methods ◽  
Mixed Optimization ◽  
Major Occupation

With the continuous growth of energy consumption, the rationalization of energy has become a priority. The photovoltaic energy sector remains a major occupation for researchers in the field of production optimization or storage methods. The concept developed in this work is a mixed optimization approach for energy management during battery charging with a duty cycle. A selective collaborative algorithm intervenes to choose and use the appropriate results of the few techniques to optimize the charging time of a battery and estimate its state of charge by using the minimum possible tools. This is done using a collective database that is accessible in real time. It also effectively allows the synchronization of information between several customers. This approach is performed on a mobile application on android, through a Google Firebase platform that allows us to secure collaborative access between multiple customers and use the results of the calculations of some algorithms. It gives us the values obtained by the various sensors in real time to accelerate the charging speed of the battery. The validation of this approach led us to practice a few scenarios using an Arduino board to show that this approach has a better performance.

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