Size and Cost Optimization of AutoCAD Oil and Gas Control Flow Designs Using Constraint Satisfaction Problem and Machine Learning

2018 ◽  
Vol 6 (5) ◽  
pp. 550-555
Author(s):  
H.A. Kore ◽  
◽  
◽  
◽  
S.B. Mane ◽  
...  
Keyword(s):  
Machine Learning ◽  
Constraint Satisfaction ◽  
Oil And Gas ◽  
Constraint Satisfaction Problem ◽  
Cost Optimization ◽  
Control Flow ◽  
Gas Control

scholarly journals Minimizing Cost Travel in Multimodal Transport Using Advanced Relation Transitive Closure

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Rachid Oucheikh ◽  
Ismail Berrada ◽  
Lahcen Omari
Keyword(s):  
Binary Relation ◽  
Constraint Satisfaction ◽  
Operations Research ◽  
Transitive Closure ◽  
Constraint Satisfaction Problem ◽  
Cost Optimization ◽  
Binary Relations ◽  
Mathematical Background ◽  
Multimodal Transport ◽  
Binary Constraint

The optimization computation is an essential transversal branch of operations research which is primordial in many technical fields: transport, finance, networks, energy, learning, etc. In fact, it aims to minimize the resource consumption and maximize the generated profits. This work provides a new method for cost optimization which can be applied either on path optimization for graphs or on binary constraint reduction for Constraint Satisfaction Problem (CSP). It is about the computing of the “transitive closure of a given binary relation with respect to a property.” Thus, this paper introduces the mathematical background for the transitive closure of binary relations. Then, it gives the algorithms for computing the closure of a binary relation according to another one. The elaborated algorithms are shown to be polynomial. Since this technique is of great interest, we show its applications in some important industrial fields.

Machine learning for simulation-based support of early collaborative design

1998 ◽  
Vol 12 (2) ◽  
pp. 123-139 ◽  
Author(s):  
NENAD IVEZIC ◽  
JAMES H. GARRETT
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Constraint Satisfaction ◽  
Collaborative Design ◽  
Constraint Satisfaction Problem ◽  
Neural Network Approach ◽  
Design Processes ◽  
Simulation Based ◽  
And Performance ◽  
Categorization Scheme

The research and development of a simulation-based decision support system (SB-DSS) capable of assisting early collaborative design processes is presented. The requirements for such a system are included. Existing collaborative DSSs are shown to lack the capability to manipulate complex simulation-based relationships. On the other hand, advances within the machine learning in design community are shown to have a potential for providing, but have not yet addressed, simulation-based support for collaborative design processes. The developed SB-DSS is described in terms of its four principal components. First, the behavior-evaluation (BE) model is used to both structure individual, domain-specific decision models and organize these models into a collaborative decision model. Second, a probabilistic framework for the BE model enables management of the uncertainty inherent in learning and using simulation-based knowledge. Significantly, this framework provides a constraint satisfaction environment in which simulation-based knowledge is used. Third, a statistical neural network approach is used to capture simulation-based knowledge and build the probabilistic behavior models based on this knowledge. Fourth, since a probability distribution theory does not exist for the nonlinear neural network approaches, Monte Carlo simulation is introduced as a method to sample the trained neural networks and approximate the likelihoods of design variable values. Consequently, constraint satisfaction problem-solving capability is obtained. In addition, a mapping of the SB-DSS architecture onto a collaborative design agent framework is provided. Experimental evaluation of a prototype SB-DSS system is summarized, and performance of the SB-DSS with respect to search and usability metrics is documented. Initial results in developing the simulation-based support for collaborative design are encouraging. Lastly, a categorization of the machine learning approach and a critique of the proposed categorization scheme is presented.

“PIRAMIDA TINGGI, A State of the Art to Fulfill Obligation of Forestry Permit on Watershed Rehabilitation at PT Pertamina Hulu Mahakam”

2020 ◽  
Author(s):  
P. Sarwanto
Keyword(s):  
Land Tenure ◽  
Management System ◽  
Web Application ◽  
Local Community ◽  
Oil And Gas ◽  
Cost Optimization ◽  
Field Work ◽  
Development Goals ◽  
Potential Risks ◽  
Oil And Gas Sector

Among other obligations imposed under the forestry permit, watershed rehabilitation planting is perceived by the upstream oil and gas sector as the most complex challenge to conquer. Despite its poor track in fulfilling timeline and required result, there are also other challenges to consider, for instance lack of critical location, weather, fire, land tenure, community habit and capability, and cost optimization. In attempt to respond these challenges, an innovation in management system is constructed at PT Pertamina Hulu Mahakam, embracing and tailoring all related challenges, difficulties, and complexities, escalating the activity to be beyond compliance. So that it will be able to deliver more than merely avoid the identified potential risks towards company. The management system, called PIRAMIDA TINGGI (Pemberdayaan Masyarakat untuk Melestarikan Hutan di Dunia demi Ketahanan Energi Nasional), actively involves government, community, and business sector as equilateral triangle that work together to perform watershed rehabilitation planting. Developed using ISO 9001:2015 process approach namely PDCA (Plan-Do-Check-Act), the PIRAMIDA TINGGI system is in line as well with NAWACITA (President Joko Widodo’s vision, mission and program). To encounter other issue found during field work, this system is equipped as well with another innovation tool named PARIDA, a geospatial mobile-desk top-web application that easily able to map and identify vegetation in real time for further geo-analyzing multi-purposes, to be operated by local community. Full set implementation of this system has benefitted all parties. To Company in form of significant cost efficiency around 13.9 MUSD and 7 days’ faster result delivery besides obligation fulfillment, for others in form of broader advantage of proven sustainability project that has gave contribution to 5P (People, Planet, Prosperity, Partnership and Peace), objectives required by UN Sustainable Development Goals 2030.

scholarly journals Permutation groups with small orbit growth

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Manuel Bodirsky ◽  
Bertalan Bodor
Keyword(s):  
Automorphism Group ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Permutation Groups ◽  
First Order ◽  
Finite Covers

Abstract Let K exp + \mathcal{K}_{{\operatorname{exp}}{+}} be the class of all structures 𝔄 such that the automorphism group of 𝔄 has at most c ⁢ n d ⁢ n cn^{dn} orbits in its componentwise action on the set of 𝑛-tuples with pairwise distinct entries, for some constants c , d c,d with d < 1 d<1 . We show that K exp + \mathcal{K}_{{\operatorname{exp}}{+}} is precisely the class of finite covers of first-order reducts of unary structures, and also that K exp + \mathcal{K}_{{\operatorname{exp}}{+}} is precisely the class of first-order reducts of finite covers of unary structures. It follows that the class of first-order reducts of finite covers of unary structures is closed under taking model companions and model-complete cores, which is an important property when studying the constraint satisfaction problem for structures from K exp + \mathcal{K}_{{\operatorname{exp}}{+}} . We also show that Thomas’ conjecture holds for K exp + \mathcal{K}_{{\operatorname{exp}}{+}} : all structures in K exp + \mathcal{K}_{{\operatorname{exp}}{+}} have finitely many first-order reducts up to first-order interdefinability.

Designing CO2 Transmission Pipelines Without Crack Arrestors

2010 ◽  
Author(s):  
Graeme G. King ◽  
Satish Kumar
Keyword(s):  
Wall Thickness ◽  
Carbon Capture ◽  
Power Plants ◽  
Oil And Gas ◽  
Cost Optimization ◽  
Operating Conditions ◽  
Operating Pressure ◽  
Design Work ◽  
Industrial Facilities ◽  
Pipeline Network

Masdar is developing several carbon capture projects from power plants, smelters, steel works, industrial facilities and oil and gas processing plants in Abu Dhabi in a phased series of projects. Captured CO2 will be transported in a new national CO2 pipeline network with a nominal capacity of 20×106 T/y to oil reservoirs where it will be injected for reservoir management and sequestration. Design of the pipeline network considered three primary factors in the selection of wall thickness and toughness, (a) steady and transient operating conditions, (b) prevention of longitudinal ductile fractures and (c) optimization of total project owning and operating costs. The paper explains how the three factors affect wall thickness and toughness. It sets out code requirements that must be satisfied when choosing wall thickness and gives details of how to calculate toughness to prevent propagation of long ductile fracture in CO2 pipelines. It then uses cost optimization to resolve contention between the different requirements and arrive at a safe and economical pipeline design. The design work selected a design pressure of 24.5 MPa, well above the critical point for CO2 and much higher than is normally seen in conventional oil and gas pipelines. Despite its high operating pressure, the proposed network will be one of the safest pipeline systems in the world today.

Application of machine learning algorithms to predict permeability in tight sandstone formations

Nafta-Gaz ◽  
2021 ◽  
Vol 77 (5) ◽  
pp. 283-292
Author(s):  
Tomasz Topór ◽  
Keyword(s):  
Machine Learning ◽  
Oil And Gas ◽  
Learning Algorithms ◽  
Confining Pressure ◽  
Machine Learning Algorithms ◽  
Core Material ◽  
Confining Stress ◽  
Tight Sandstone ◽  
Oil And Gas Exploration ◽  
Better Than

The application of machine learning algorithms in petroleum geology has opened a new chapter in oil and gas exploration. Machine learning algorithms have been successfully used to predict crucial petrophysical properties when characterizing reservoirs. This study utilizes the concept of machine learning to predict permeability under confining stress conditions for samples from tight sandstone formations. The models were constructed using two machine learning algorithms of varying complexity (multiple linear regression [MLR] and random forests [RF]) and trained on a dataset that combined basic well information, basic petrophysical data, and rock type from a visual inspection of the core material. The RF algorithm underwent feature engineering to increase the number of predictors in the models. In order to check the training models’ robustness, 10-fold cross-validation was performed. The MLR and RF applications demonstrated that both algorithms can accurately predict permeability under constant confining pressure (R2 0.800 vs. 0.834). The RF accuracy was about 3% better than that of the MLR and about 6% better than the linear reference regression (LR) that utilized only porosity. Porosity was the most influential feature of the models’ performance. In the case of RF, the depth was also significant in the permeability predictions, which could be evidence of hidden interactions between the variables of porosity and depth. The local interpretation revealed the common features among outliers. Both the training and testing sets had moderate-low porosity (3–10%) and a lack of fractures. In the test set, calcite or quartz cementation also led to poor permeability predictions. The workflow that utilizes the tidymodels concept will be further applied in more complex examples to predict spatial petrophysical features from seismic attributes using various machine learning algorithms.

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