Numerical Investigation of Sand-Screen Performance in the Presence of Adhesive Effects for Enhanced Sand Control

SPE Journal ◽  
2019 ◽  
Vol 24 (05) ◽  
pp. 2195-2208 ◽  
Author(s):  
Siti Nur Shaffee ◽  
Paul F. Luckham ◽  
Omar K. Matar ◽  
Aditya Karnik ◽  
Mohd Shahrul Zamberi
Keyword(s):  
Management Practices ◽  
Oil And Gas ◽  
Voronoi Tessellation ◽  
Oil And Gas Industry ◽  
Solid Particles ◽  
Particle Adhesion ◽  
Sand Production ◽  
Gas Industry ◽  
Particle Filtration ◽  
Sand Control

Summary In many industrial processes, an effective particle–filtration system is essential for removing unwanted solids. The oil and gas industry has explored various technologies to control and manage excessive sand production, such as by installing sand screens or injecting consolidation chemicals in sand–prone wells as part of sand–management practices. However, for an unconsolidated sandstone formation, the selection and design of effective sand control remains a challenge. In recent years, the use of a computational technique known as the discrete–element method (DEM) has been explored to gain insight into the various parameters affecting sand–screen–retention behavior and the optimization of various types of sand screens (Mondal et al. 2011, 2012, 2016; Feng et al. 2012; Wu et al. 2016). In this paper, we investigate the effectiveness of particle filtration using a fully coupled computational–fluid–dynamics (CFD)/DEM approach featuring polydispersed, adhesive solid particles. We found that an increase in particle adhesion reduces the amount of solid in the liquid filtrate that passes through the opening of a wire–wrapped screen, and that a solid pack of particle agglomerates is formed over the screen with time. We also determined that increasing particle adhesion gives rise to a decrease in packing density and a diminished pressure drop across the solid pack covering the screen. This finding is further supported by a Voronoi tessellation analysis, which reveals an increase in porosity of the solid pack with elevated particle adhesion. The results of this study demonstrate that increasing the level of particle agglomeration, such as by using an adhesion–promoting chemical additive, has beneficial effects on particle filtration. An important application of these findings is the design and optimization of sand–control processes for a hydrocarbon well with excessive sand production, which is a major challenge in the oil and gas industry.

Evaluation of Machine-Learning Tools for Predicting Sand Production

2021 ◽  
Author(s):  
Afungchwi Ronald Ngwashi ◽  
David O. Ogbe ◽  
Dickson O. Udebhulu
Keyword(s):  
Machine Learning ◽  
Niger Delta ◽  
Oil And Gas ◽  
Back Propagation ◽  
Oil And Gas Industry ◽  
Learning Tools ◽  
Sand Production ◽  
Data Set ◽  
Test Set ◽  
Gas Industry

Abstract Data analytics has only recently picked the interest of the oil and gas industry as it has made data visualization much simpler, faster, and cost-effective. This is driven by the promising innovative techniques in developing artificial intelligence and machine-learning tools to provide sustainable solutions to ever-increasing problems of the petroleum industry activities. Sand production is one of these real issues faced by the oil and gas industry. Understanding whether a well will produce sand or not is the foundation of every completion job in sandstone formations. The Niger Delta Province is a region characterized by friable and unconsolidated sandstones, therefore it's more prone to sanding. It is economically unattractive in this region to design sand equipment for a well that will not produce sand. This paper is aimed at developing a fast and more accurate machine-learning algorithm to predict sanding in sandstone formations. A two-layered Artificial Neural Network (ANN) with back-propagation algorithm was developed using PYTHON programming language. The algorithm uses 11 geological and reservoir parameters that are associated with the onset of sanding. These parameters include depth, overburden, pore pressure, maximum and minimum horizontal stresses, well azimuth, well inclination, Poisson's ratio, Young's Modulus, friction angle, and shale content. Data typical of the Niger Delta were collected to validate the algorithm. The data was further split into a training set (70%) and a test set (30%). Statistical analyses of the data yielded correlations between the parameters and were plotted for better visualization. The accuracy of the ANN algorithm is found to depend on the number of parameters, number of epochs, and the size of the data set. For a completion engineer, the answer to the question of whether or not a well will require sand production control is binary-either a well will produce sand or it does not. Support vector machines (SVM) are known to be better suited as the machine-learning tools for binary identification. This study also presents a comparative analysis between ANN and SVM models as tools for predicting sand production. Analysis of the Niger Delta data set indicated that SVM outperformed ANN model even when the training data set is sparse. Using the 30% test set, ANN gives an accuracy, precision, recall, and F1 - Score of about 80% while the SVM performance was 100% for the four metrics. It is then concluded that machine learning tools such as ANN with back-propagation and SVM are simple, accurate, and easy-to-use tools for effectively predicting sand production.

scholarly journals Human Resource Management Practices and Organizational Performance: An Empirical Study of Oil and Gas Industry in Nigeria

2020 ◽  
Vol 7 (1) ◽  
pp. 53-69
Author(s):  
Moses M. Adagbabiri ◽  
Ugo Chuks Okolie
Keyword(s):  
Resource Management ◽  
Human Resource Management ◽  
Organizational Performance ◽  
Human Resource ◽  
Management Practices ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Hrm Practices ◽  
Human Resource Management Practices

The impact of human resource management (HRM) practices on organizational performance has been subject of discourse among social scientists from a wide range of disciplines in the last two decades. But unfortunately, very insufficient number of studies in this area has been conducted in Nigeria and other developing countries. This study was undertaken to fill this obvious research gap. The author applied descriptive method and collected the data via a survey of 164 respondents in Nigerias Oil and Gas Industry. Data collected were analyzed using Pearson product moment correlation and t-test analysis. The study found that there is a significant relationship between HRM practices and organizational performance. As predicted, the study revealed that human resource management practices exert positive and statistically significant impact on organizational performance. Requisite conclusion and recommendations were provided in the light of theoretical and empirical findings. With this study, we hope to contribute to a better understanding of the role of HRM practices in creating and sustaining organizational performance, specifically in the Nigerian context.

scholarly journals CFD STUDY OF SAND EROSION IN PIPELINE USING DIFFERENT PERCENTAGE OF ETHYLENE GLYCOL AND DEIONIZED WATER

2020 ◽  
Vol 9 (12) ◽  
pp. 111-125
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Skin Friction Coefficient ◽  
Solid Particles ◽  
Sand Particle ◽  
Pipe Bend ◽  
Gas Industry ◽  
Oil Reserves ◽  
Sand Erosion ◽  
Swirl Velocity

Sand productions are inclusive of various types of major key challenges for gas and oil productions as the sand managements are rapidly growing in becoming significant to manage wells of high rates. Since approximately 70% of gas as well oil reserves around the globe are sand formations Sand production is an unavoidable by-product in oil and gas industry as 70% gas and oil reserves of the world oil are sand formation. Transportation of the particles from the wellbore to the surface will cause the damage to the amenities and tools. Wells producing gas and oils can potentially fail because of the erosion of the major solid particles. It can be illustrated through an example like producing wells having considerable amount of production of sand might affect negatively over the fitting and components of the pipeline, well tubing as well as the equipment used for the production. Thus can cause highly priced potential ecofriendly damages, equipment loss and downtime production. The current study provides outcomes gathered through examining and analyzing various factors for determining the severities and amount of the erosion of sand over the pipe bend. To solve the phenomena of the flow of the fluid, this study has used CFD. To design the pipe’s elbow, CATIA-V5 is brought in use and meshing is done with the help of the ANSYS. Different cases will be studied here by varying the percentage of water and EG with respect to sand particle size 160m and 370m. Erosion rate, Skin friction Coefficient and Swirl velocity are the three major effects which will be studied further. Through the observation of the study, it can be said that material’s character and flow velocity are the predominant factors which might affect the rate of sand erosion within the pipelines. The observation is made over every factor and is also analyzed.

scholarly journals Mediation Analysis Using the Hierarchical Multiple Regression Technique: A Study of the Mediating Roles of World-Class Performance in Operations

2010 ◽  
Vol 12 (2) ◽  
pp. 139 ◽  
Author(s):  
Wakhid S. Ciptono ◽  
Abdul Razak Ibrahim ◽  
Ainin Sulaiman
Keyword(s):  
Quality Management ◽  
Financial Performance ◽  
Management Practices ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Critical Factors ◽  
Gas Industry ◽  
Quality Management Practices ◽  
World Class ◽  
Operational Excellence

The changing environment in an organization is forcing the organization to find a plan of integrated management framework and adequate performance measurement. Failure to plan basically means planning failure for the business. Finding the critical factors of quality management practices (QMP), themediating roles of the contextual factors of world-class performance in operations (i.e., world-class company practices or WCC, operational excellence practices or OE, company nonfinancial performance or CNFP), and the company financial performance would enable the company to facilitate the sustainability of TQM implementation model.This empirical study aims to assess how TQM—a holistic management philosophy initially developed by W. Edward Deming, which integrates improvement strategy, management practices, and organizational performance—is specifically implemented in the oil and gas companies operating in Indonesia. Relevant literature on the TQM, the world-class performance in operations (world-class company and operational performance), the company performance (financial and non-financial performances), and the amendments of the Law of the Republic of Indonesia concerning the oil and gas industry, and related research on how the oil and gas industry in Indonesia develops sustainable competitive advantage and sustainable development programs are reviewed in details in our study. The findings from data analysis provide evidence that there is a strong positive relationship between the critical factors of quality management practices and the company financial performance mediated by the three mediating variables, i.e., world-class company practices, operational excellence practices, and company non-financial performance.

scholarly journals Resilience beyond Formal Structures: A Network Perspective towards the Challenges of an Aging Workforce in the Oil and Gas Industry

2019 ◽  
Vol 5 (1) ◽  
pp. 15 ◽  
Author(s):  
Fabio Bento ◽  
Luciano Garotti
Keyword(s):  
Network Analysis ◽  
Management Practices ◽  
Explicit Knowledge ◽  
Oil And Gas ◽  
Initial Conditions ◽  
Social Systems ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Knowledge Loss ◽  
Logic Network

Changes in workplace demographics in the oil and gas industry have raised a concern about the risks of a knowledge-loss crisis due to mass retirement. The industry response has often consisted of strategies aimed at mapping knowledge across organizational units, codifying knowledge in databases, and mentoring new staff. However, such common managerial responses show important limitations in terms of grasping tacit and network-based dimensions of knowledge in complex oil production operations. Therefore, there is an industrial need for innovative knowledge management practices. In this conceptual article, we look at the knowledge-loss crisis from the perspective of network resilience in complex systems. A central assumption here is that it is important to look at retiring staff not only in terms of their explicit knowledge, but also in relation to their roles in evolving networks of interactions. Why do some social systems adapt to the departure of some individuals, recover from eventual knowledge-loss crises, and keep performing its functions? From an anticipatory logic, network analysis may show the initial conditions of a system and identify possible loss scenarios. From an adaptive logic, network analysis may inform interventions aimed at facilitating processes of interactions from which new knowledge may emerge and spread. Integrated operations may be a step in this direction.

Experimental Characterization of Particle-Laden Air Flow in a Horizontal Pipe

2015 ◽  
Author(s):  
Y. Dai ◽  
T. S. Khan ◽  
M. S. Alshehhi ◽  
L. Khezzar
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Solid Particles ◽  
Horizontal Pipe ◽  
Gas Industry ◽  
Black Powder ◽  
Powder Samples ◽  
Flow Through

In many engineering applications, movement of micron and submicron size solid particles with compressed air or gas causes major engineering problems as in the case of black powder in oil and gas industry. Therefore, understanding its physical and flow dynamics characteristics inside a pipeline can be very useful to efficiently manage pipelines contamination issues. This paper presents an experimental study carried out to simulate characteristics of air-sand particles flow through a transparent horizontal pipe with various flow conditions. Experimental analysis focuses on determination of critical pickup velocity of the solid particles and measurement of pressure drop across the sand bed of various blockage ratios. Results have been compared with previous studies in literature. Limited experiments are conducted using black powder samples as well. Comparison of results shows vast deviation between sand and black powder behavior.

The State of the Art and Challenges in Geomechanical Modeling of Injector Wells: A Review Paper

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
J. F. Bautista ◽  
A. Dahi Taleghani
Keyword(s):  
Oil And Gas ◽  
Review Paper ◽  
State Of The Art ◽  
Oil And Gas Industry ◽  
Fluid Injection ◽  
Well Performance ◽  
Sand Production ◽  
Gas Industry ◽  
Injection Process ◽  
Local Compaction

Fluid injection is a common practice in the oil and gas industry found in many applications such as waterflooding and disposal of produced fluids. Maintaining high injection rates is crucial to guarantee the economic success of these projects; however, there are geomechanical risks and difficulties involved in this process that may threat the viability of fluid injection projects. Near wellbore reduction of permeability due to pore plugging, formation failure, out of zone injection, sand production, and local compaction are challenging the effectiveness of the injection process. Due to these complications, modeling and simulation has been used as an effective tool to assess injectors' performance; however, different problems have yet to be addressed. In this paper, we review some of these challenges and the solutions that have been proposed as a primary step to understand mechanisms affecting well performance.

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