scholarly journals Modified approach for identifying weak zones for effective sand management

2019 ◽  
Vol 10 (2) ◽  
pp. 537-555
Author(s):  
Aliyu Adebayo Sulaimon ◽  
Lim Lee Teng
Keyword(s):  
Shear Modulus ◽  
Transition Zone ◽  
Oil And Gas ◽  
Field Tests ◽  
Oil And Gas Industry ◽  
Production Equipment ◽  
Sand Production ◽  
Geomechanical Properties ◽  
Wellbore Pressure ◽  
Weak Zones

Abstract Sand production is a major problem that the oil and gas industry has been facing for years. It can lead to loss of production, equipment damage or complete well abandonment. Prediction of sand has been historically challenging due to the periodic nature of sand production, insufficient laboratory tests and lack of field tests validation. Analyses have been performed to identify weak zones for planned wells, and common technique is the application of shear modulus and mechanical properties log (MPL) criteria developed by Tixier et al. (J Pet Technol 27:283–293, 1975). However, the set criteria have been found to be generally inadequate to detect transition zone or predict weak formation in some fields. In this study, using the knowledge of rock behavior, geomechanical properties and well log data, we have established new simple criteria for identifying fragile sections within a transition zone. In situ logging data from a field X, located in Sabah, Malaysia, and Field Y, located in Shimokita, Japan, were used in this study. Using the threshold for shear modulus and MPL, the criteria for the geomechanical properties are set to differentiate formation strengths at different depths. The threshold for Poisson’s ratio is 0.34, Young’s modulus at 1.6 × 106 psi and the unconfined compressive strength at 2400 psi. The MPL and geomechanical models were generated to predict sanding incident. The results were subsequently validated with artificial neural network using MATLAB. Also, critical wellbore pressure is calculated and acts as a guide to operate outside the sand failure envelope. Thus, the prediction of the weak formation using geomechanical properties has been further established in this study.

Applied Mechanics Problems in the Oil and Gas Industry

1986 ◽  
Vol 39 (11) ◽  
pp. 1687-1696 ◽  
Author(s):  
Jean-Claude Roegiers
Keyword(s):  
Oil And Gas ◽  
Petroleum Industry ◽  
Oil Industry ◽  
Oil And Gas Industry ◽  
Production Equipment ◽  
Paper Briefly ◽  
Research Activity ◽  
Gas Industry ◽  
Well Production ◽  
Current Research Activity

The petroleum industry offers a broad spectrum of problems that falls within the domain of expertise of mechanical engineers. These problems range from the design of well production equipment to the evaluation of formation responses to production and stimulation. This paper briefly describes various aspects and related difficulties with which the oil industry has to deal, from the time the well is spudded until the field is abandoned. It attempts to delineate the problems, to outline the approaches presently used, and to discuss areas where additional research is needed. Areas of current research activity also are described; whenever appropriate, typical or pertinent case histories are used to illustrate a point.

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.

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.

Advances from Arctic Oil Spill Response Research

2017 ◽  
Vol 2017 (1) ◽  
pp. 1487-1506 ◽  
Author(s):  
Joseph V. Mullin
Keyword(s):  
Oil Spill ◽  
Large Scale ◽  
Oil And Gas ◽  
Field Tests ◽  
Field Research ◽  
Oil And Gas Industry ◽  
The Arctic ◽  
Research Projects ◽  
Effective Response ◽  
Oil Spill Response

Abstract 2017-161 Over the past four decades, the oil and gas industry has made significant advances in being able to detect, contain and clean up spills and mitigate the residual consequences in Arctic environments. Many of these advances were achieved through collaborative research programs involving industry, academic and government partners. The Arctic Oil Spill Response Technology - Joint Industry Programme (JIP), was launched in 2012 and completed in early 2017 with the objectives of building on an already extensive knowledge base to further improve Arctic spill response capabilities and better understand the environmental issues involved in selecting and implementing the most effective response strategies. The JIP was a collaboration of nine oil and gas companies (BP, Chevron, ConocoPhillips, Eni, ExxonMobil, North Caspian Operating Company, Shell, Statoil, and Total) and focused on six key areas of oil spill response: dispersants; environmental effects; trajectory modeling; remote sensing; mechanical recovery and in-situ burning. The JIP provided a vehicle for sharing knowledge among the participants and international research institutions and disseminating information to regulators, the public and stakeholders. The network of engaged scientists and government agencies increased opportunities to develop and test oil spill response technologies while raising awareness of industry efforts to advance the existing capabilities in Arctic oil spill response. The JIP consisted of two phases, the first included technical assessments and state of knowledge reviews resulting in a library of sixteen documents available on the JIP website. The majority of the JIP efforts focused on Phase 2, actual experiments, and included laboratory, small and medium scale tank tests, and field research experiments. Three large-scale field tests were conducted in the winter and spring months of 2014–2016 including recent participation of the JIP in the 2016 NOFO oil on water exercise off Norway. The JIP was the largest pan-industry programme dedicated to oil spill response in the Arctic, ever carried out. Twenty seven research projects were successfully and safely conducted by the world’s foremost experts on oil spill response from across industry, academia, and independent scientific institutions in ten countries. The overarching goal of the research was to address the differing aspects involved in oil spill response, including the methods used, and their applicability to the Arctic’s unique conditions. All research projects were conducted using established protocols and proven scientific technologies, some of which were especially adjusted for ice conditions. This paper describes the scope of the research conducted, results, and key findings. The JIP is committed to full transparency in disseminating the results through peer reviewed journal articles, and all JIP research reports are available free of charge at www.arcticresponsetechnology.org.

New «green» inhibitors of gas hydrate formation for the oil and gas industry based on polysaccharides

2021 ◽  
pp. 33-40
Author(s):  
V.A. Dokichev ◽  
◽  
A.I. Voloshin ◽  
N.E. Nifantiev ◽  
M.P. Egorov ◽  
...  
Keyword(s):  
Gas Hydrate ◽  
Oil And Gas ◽  
Hydrate Formation ◽  
Field Tests ◽  
Oil And Gas Industry ◽  
Quasi Equilibrium ◽  
Green Inhibitor ◽  
Gas Industry ◽  
Gas Hydrate Formation ◽  
Thermobaric Conditions

The thermobaric conditions for the formation of gas hydrates in the presence of the sodium salt of carboxymethylcellulose, dextran, and arabinogalactan were studied in a quasi-equilibrium thermodynamic experiment. It is established that polysaccharides slow down the rate and change the conditions of gas hydrate formation of a mixture of natural gases, showing the properties of a thermodynamic and kinetic inhibitor with technological efficiency exceeding methanol by 170-270 times when used in the same dosages. The results of the development of a «green» synergistic inhibitor of gas hydrate formation «Glycan RU» on their basis are presented, which includes a combination of thermodynamic and kinetic inhibitors. Pilot field tests of «Glycan RU» were carried out at the wells of the Priobskoye, Prirazlomnoye, Ombinsky, Zapadno-Ugutskoye oilfields. It was found that at dosages of 1000 g/m3 and 500 g/m3, there is no formation of hydrate plugs in the annulus. «Glycan RU» is recommended for industrial use by the technology of periodic injection and/or continuous dosing through wellhead dispensers. Keywords: carboxymethylcellulose; dextran; arabinogalactan; polysaccharides; «green» inhibitor of gas hydrate formation; «Glycan RU».

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.

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

2016 ◽  
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 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.

scholarly journals VERTICAL CAPACITY OF BUCKET FOUNDATIONS IN UNDRAINED SOIL

2014 ◽  
Vol 20 (3) ◽  
pp. 360-371 ◽  
Author(s):  
Amin Barari ◽  
Lars Bo Ibsen
Keyword(s):  
Wind Turbine ◽  
Oil And Gas ◽  
Field Tests ◽  
Oil And Gas Industry ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Vertical Load ◽  
Gas Industry ◽  
Offshore Oil And Gas ◽  
Offshore Oil

Offshore wind turbine structures are traditionally founded on gravity concrete foundations or mono-piles. Bucket foundations were developed for the offshore oil and gas industry and are now being used in wind turbine construction. The loading in this application is characterized by a vertical load due to the slender construction combined with horizontal forces inducing a large overturning moment. Field tests on bucket foundations were performed to gain insight into the vertical load response of bucket foundations in clay soils. The field tests were accompanied by finite element numerical simulations in order to provide a better understanding of the parameters influencing bucket foundation behaviour.

Application of Acoustic Impedance and Reflectivity in Reservoir Characterization

2021 ◽  
Author(s):  
Stanley Oifoghe ◽  
Ikenna Obodozie ◽  
Lucrecia Grigoletto
Keyword(s):  
Acoustic Impedance ◽  
Oil And Gas ◽  
Reservoir Characterization ◽  
Water Saturation ◽  
Oil And Gas Industry ◽  
Well Log ◽  
Volume Calculation ◽  
Geomechanical Properties ◽  
Volume Porosity ◽  
Well Log Analysis

Abstract Well log analysis is one of the methods for reservoir characterization, in the oil and gas industry. Logs are used for subsurface formation evaluation. They are useful in hydrocarbon zone identification and volume calculation. Interpretation of well log involves sequential steps, which are lithology, shale volume, porosity and saturation determination. It is unwise to analyze well log without following the logical steps, as this could introduce errors in the result. Petrophysical and Geomechanical properties are two classes of properties for reservoir characterization. The computed volume of shale in the reservoir was 10%, the average water saturation was 30%, and the average porosity was 25pu. The bulk density decreased from 2.15g/cc to 1.95g/cc and there is a considerably lower acoustic impedance in the hydrocarbon bearing sands. In challenging reservoirs, where traditional petrophysical methods do not give definitive results, the use of geomechanical methods will improve interpretation certainty and help to clear doubts in the interpreted results.

Geomechanical Properties Estimation Utilizing Artificial Intelligence Prediction Tool

2021 ◽  
Author(s):  
Mohammed Alabbad ◽  
Mohammad Alqam ◽  
Hussain Aljeshi
Keyword(s):  
Mechanical Properties ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Significant Loss ◽  
Wellbore Stability ◽  
Rock Properties ◽  
Ann Model ◽  
Full Field ◽  
Geomechanical Properties ◽  
Rock Mechanical Properties

Abstract Drilling and fracturing are considered to be one of the major costs in the oil and gas industry. Cost may reach tens of millions of dollars and improper design may lead to significant loss of money and time. Reliable fracturing and drilling designs are governed with decent and representative rock mechanical properties. Such properties are measured mainly by analyzing multiple previously cored wells in the same formation. The nature of the conducted tests on the collected plugs are destructive and samples cannot be restored after performing the rock mechanical testing. This may disable further evaluation on the same plugs. This study aims to build an artificial neural network (ANN) model that is capable of predicting the main rock mechanical properties, such as Poisson's ratio and compressive strength from already available lab and field measurements. The log data will be combined together with preliminary lab rock properties to build a smart model capable of predicting advance rock mechanical properties. Hence, the model will provide initial rock mechanical properties that are estimated almost immediately and without undergoing costly and timely rock mechanical laboratory tests. The study will also give an advantage to performing preliminary estimates of such parameters without the need for destructive mechanical core testing. The ultimate goal is to draw a full field geomechanical mapping with this tool rather than having localized scattered data. The AI tool will be trained utilizing representative sets of rock mechanical data with multiple feed-forward backpropagation learning techniques. The study will help in localizing future well location and optimizing multi-stage fracturing designs. These produced data are needed for upstream applications such as wellbore stability, sanding tendency, hydraulic fracturing, and horizontal/multi-lateral drilling.

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