Day 4 Thu, November 18, 2021最新文献

{"title":"Directional Radial Drilling Increases Reservoir Coverage with Precise Wellbore Placement Resulting in a Significant Production Increase from a Thin Reservoir","authors":"A. Bashirov, I. Galas, M. Nazyrov, D. Kuznetsov, A.A. Akkuzhin","doi":"10.2118/208035-ms","DOIUrl":"https://doi.org/10.2118/208035-ms","url":null,"abstract":"\u0000 In many oil and gas provinces not only in Russia, but throughout the world, carbonate strata make up a significant portion of the sedimentary cover, and large accumulations of hydrocarbons are associated with them. However, the purposeful study of them as reservoirs for hydrocarbons in our country practically began only in the post-war years. In the special petrography laboratory carbonate rocks composing various stratigraphic complexes of almost all oil and gas provinces of the Soviet Union were studied, and in particular, Paleozoic carbonate strata of the Timan-Pechora province, Ural-Volga region, Belarus, Kazakhstan, ancient Riphean-Cambrian formations of Yakutia and relatively young strata of the Late Cretaceous of the northeastern Ciscaucasia.\u0000 Carbonates are widespread sedimentary rocks. A very significant part of them was formed in the conditions of vast shallow-water marine epicontinental basins. A large number of works are devoted to the study of such deposits. However, issues related to the conditions of formation of carbonate sediments and their postsedimentary changes cannot be considered resolved, as well as the classification of the rocks themselves.\u0000 The analyzed field is the Osvanyurskoye one. It was discovered in 2007. The field is located in the north-east of the European part of the Russian Federation, 2 km from Usinsk in the Komi Republic. The field is a part of the Timano-Pechora oil and gas province and it is a mature field (fig. 1). The objective was a 2.5m thick layer of the Serpukhov horizon.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"222 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77433067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning in Computer Vision Software for Geomechanics Modeling","authors":"A. Noufal, Jaijith Sreekantan, Rachid Belmeskine, M. Amri, A. Benaichouche","doi":"10.2118/208049-ms","DOIUrl":"https://doi.org/10.2118/208049-ms","url":null,"abstract":"\u0000 AI-GEM (Artificial Intelligence of Geomechanics Earth Modelling) tool aims to detect the geomechanical features, especially the elastic parameters and stresses. Characterizing the wellbore instability issues is one of the factors increases cost of drilling and creating an AI-based tool will enhance and present a real-time solution for wellbore instability. These features are usually interpreted manually, depending on the experience and usually impacted by inconsistencies due to biased or unexperienced interpreters. Therefore, there is a need for a robust automatic or semiautomatic approach to reduce time, manual efficiency and consistency.\u0000 The range of Geomechanics issues is wide and interfaces with many other upstream disciplines (e.g., Petrophysics, Geophysics, Production Geology, Drilling and Reservoir Engineering). Safe and effective field operation is built on the understanding and implementation of the subsurface in-situ stress state throughout the life of the field; the quantification of key subsurface uncertainties through well thought-out data gathering and characterization programs. The integration with appropriate Geomechanics modelling and the field surveillance /monitoring strategy.\u0000 There are two major aspects that must be addressed during the design phase of any Geomechanics project. The first and most important is developing a realistic estimate of the expected mechanical behaviour of the rocks and its potential response as a result of drilling. The second is to design an economic, safe well and support method for the determined rocks behaviour. The design process begins with the feasibility study followed by preliminary design, the detail design, tender design and throughout the construction. The design is constantly updated during each phase as more information becomes available and this requires the involvement of Geologists, Engineers and Subject Matter Expert throughout the phases of a project. A central concern for all geomechanical designs is the well-rock interaction, which is not only includes the final state but also the transient effects of the well processes as well as time and stress of the dependent rock properties.\u0000 The end-to-end workflow to achieve the mechanical earth model is automated, guided and orchestrated with the help of machine learning framework such as recommendation engine for offset well data, prediction of well logs, and optimization for all calibration with existing test results, enabling end users to run sensitivity and scenario analysis so on and so forth.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80784314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Rock Typing Methods Including Empirical, Deterministic, Statistical, Probabilistic, Predictive Techniques and New Applications for Practical Reservoir Characterization","authors":"G. Gunter, Mohamed Yacine Yacine Sahar, D. Allen, E. Viro, Shahin Negabahn, Mohamed Watfa","doi":"10.2118/207245-ms","DOIUrl":"https://doi.org/10.2118/207245-ms","url":null,"abstract":"\u0000 This paper discusses integrating common methods and applications for \"Rock Typing\" (also known as Petrophysical Rock Typing-PRT) including empirical, deterministic, statistical, probalistic and automatic/predictive approaches. Many industry asset teams apply one or more of these methods when creating static reservoir models, using dynamic reservoir simulations, completing petrophysical studies for saturation height models and determining reservoir volumetrics as part of reservoir characterization studies.\u0000 Our intention is to provide guidance and important information on how and when to use the various methods, so people can make an informed selection. This discussion is important as many disciplines apply these PRT techniques without understanding the pros, cons and limitations of the different methods. An important tool is comparing PRT results from multiple methods.\u0000 The topics and workflows that are covered focus on various PRT techniques and workflows. We will use case-studies to illustrate the key features and make important comparisons. Key results include comparing pros and cons, how to use and combine multiple PRT techniques and verify results.\u0000 This paper includes these techniques and workflows;MICP, core analysis and pore throat calibration.Core-Log Integration focused on PRT analysis.Winland, Pittman, Aguilera and Hartmann et.al Gameboard methods.K-Phi ratio, Flow Zone Indicators and Rock Quality Index methods.Classic, Modified and Stratigraphic Lorenz methods.IPSOM and HRA Probabilistic methods.Case Study – Super Plot and Advanced Automatic PRT Method.Special Topics – Carbonate Methods, NMR and Single Well Vertical Line.\u0000 Practical approaches based on case studies show how PRT analysis can be applied in mature fields to identify by-passed hydrocarbon zones and zones that have a high probability of producing water using open hole, cased hole and production logs. Traditional Rock Typing (PRT) analysis can be applied as a single well technique or as a multi-well method so operations teams can identify additional business opportunities (remedial workovers, infill drilling locations or exploitation targets) and compare reservoir performance with intrinsic rock properties.\u0000 New applications and additional topics cover single, multiple well approaches and new emerging PRT techniques (including NMR well logs and machine learning). We recommend how to merge classic facies with PRT analysis for 3-D applications including populating a 3D volume.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72738235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inline Drilling Fluid Property Measurement, Integration, and Modeling to Enhance Drilling Practice and Support Drilling Automation","authors":"S. Roy, S. Kamal, Richard Frazier, Ross Bruns, Yahia Ait Hamlat","doi":"10.2118/208064-ms","DOIUrl":"https://doi.org/10.2118/208064-ms","url":null,"abstract":"\u0000 Frequent, reliable, and repeatable measurements are key to the evolution of digitization of drilling information and drilling automation. While advances have been made in automating the drilling process and the use of sophisticated engineering models, machine learning techniques to optimize the process, and lack of real-time data on drilling fluid properties has long been recognized as a limiting factor. Drilling fluids play a significant function in ensuring quality well construction and completion, and in-time measurements of relevant fluid properties are key to automation and enhancing decision making that directly impacts well operations.\u0000 This paper discusses the development and application of a suite of automated fluid measurement devices that collect key fluid properties used to monitor fluid performance and drive engineering analyses without human involvement. The deployed skid-mounted devices continually and reliably measure properties such as mud weight, apparent viscosity, rheology profiles, temperatures, and emulsion stability to provide valuable insight on the current state of the fluid. Real-time data is shared with relevant rig and office- based personnel to enable process monitoring and trigger operational changes. It feeds into real-time engineering analyses tools and models to monitor performance and provides instantaneous feedback on downhole fluid behavior and impact on drilling performance based on current drilling and drilling fluid property data. Equipment reliability has been documented and demonstrated on over 30 wells and more than 400 thousand ft of lateral sections in unconventional shale drilling in the US. We will share our experience with measurement, data quality and reliability. We will also share aspects of integrating various data components at disparate time intervals into real-time engineering analyses to show how real-time measurements improve the prediction of well and wellbore integrity in ongoing drilling operations. In addition, we will discuss lessons learned from our experience, further enhancements to broaden the scope, and the integration with operators, service companies and other original equipment manufacturer in the domain to support and enhance the digital drilling ecosystem.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90940984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Well Integrity Catastrophe Avoided Through Advanced Well Integrity and Reservoir Monitoring Analysis, a Case Study","authors":"Mohamed Elyas, Sherif Aly, U. Achinanya, S. Prosvirkin, Shayma AlSaffar, Muthafar Mohammad, M. Siddiqui, Awrad Fahad","doi":"10.2118/208055-ms","DOIUrl":"https://doi.org/10.2118/208055-ms","url":null,"abstract":"\u0000 Well integrity is one of the main challenges that are facing operators, finding the source of the well problem and isolating it before a catastrophic event occurs. This study demonstrates the power of integrating different reservoir monitoring and well integrity logs to evaluate well integrity, identify the underlying cause of the potential failure, and providing a potential corrective solution.\u0000 Recently, some Injector/producer wells reported migration of injection fluids/gas into shallower sections, charging these formations and increasing the risk of compromised well integrity. Characterization of the well issues required integration of multi-detector pulsed-neutron, well integrity (multi finger caliper, multi-barrier corrosion, cement evaluation, and casing thickness measurements), high precision temperature logs and spectral noise logs. After data integration, detailed analysis was performed to specifically find the unique issues in each well and assess possible corrective actions.\u0000 The integrated well integrity logs clearly showed different 9.625-inch and 13.375-inch casings leak points. The reservoir monitoring logs showed lateral and vertical gas and water movements across Wara, Tayarat, Rus, and Radhuma formations. Cement evaluation loges showed no primary cement behind the first barrier casing which was the root cause of the problem. Therefore, the proposed solution, was a cement squeeze. Post squeeze, re-logging occurred, validating zonal isolation and a return of a standard geothermal gradient across the Tayarat formation. Most importantly, the cement evaluation identified good bond from the squeeze point clear to surface, isolating all formations. All these wells were returned to service (injector/producer), daily annular pressure monitoring confirmed that no further pressure build up was seen.\u0000 Kuwait Oil Company managed to avoid a catastrophic well integrity event on these wells and utilized the approach presented to take the proper corrective actions, and validate that the action taken resolved the initial well integrity issues. Consequently, the wells were returned to service, and the company avoided a costly high probability blowout.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73886408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Casing While Drilling Successfully Implemented for the First Time in High Risk Area of the Bahrain Field","authors":"Abdulla Ebrahim Aljawder, Ahmed Aljanahi, Hassan Almannai, Omar Ali Matar, Eyad Mohamed Ali, Gokalp Aydinak, Andrea Pincay, Ahmed Samir Refai, Zeyad Sameer Zayed","doi":"10.2118/208068-ms","DOIUrl":"https://doi.org/10.2118/208068-ms","url":null,"abstract":"\u0000 One known risk of the Awali field is hole collapsing in the surface section due to the presence of floating bloulders and cavities in areas of the field; resulting in wells being abandoned as conventional drilling was not enough to solve this issue. In 2019, Schlumberger and Tatweer Petroleum combined forces to improve production of Ostracod/Magwa shallow reservoirs by drilling wells in new areas of the field which included drilling in locations with offset wells that were abandoned due to hole collapse issues in the first 500’ interval. Drilling campaign started in june 2019 and drilled succesfully two wells, but two others were abandonned due to hole collapse issues. These abandonned wells were in very promising production areas as per the reservoir model and due to the shallowenest of the reservoir, it was not possible to move the surface location. Therefore, the issues in the surface section needed to be solved in order to maximize profits in the country.\u0000 Following a rigerous study for determining which is the optimal solution for drilling the surface section in this area of the field, Casing While Drilling (CwD) technology was selected and implemented in August 2019 in well A-1530D, next to the previous abandoned wells. CwD operation was performed with excellent results by drilling from 101ft to 520ft with no issues. CwD successfully isolated the higly problematic zones in the surface section and more importantly, allowed to reach areas of the reservoir that had high potential for production.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73587114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discrete Fracture Model for Hydro-Mechanical Coupling in Fractured Reservoirs","authors":"Xupeng He, Tian-Yang Qiao, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/208039-ms","DOIUrl":"https://doi.org/10.2118/208039-ms","url":null,"abstract":"\u0000 The process of coupled flow and mechanics occurs in various environmental and energy applications, including conventional and unconventional fractured reservoirs. This work establishes a new formulation for modeling hydro-mechanical coupling in fractured reservoirs. The discrete-fracture model (DFM), in which the porous matrix and fractures are represented explicitly in the form of unstructured grid, has been widely used to describe fluid flow in fractured formations. In this work, we extend the DFM approach for modeling coupled flow-mechanics process, in which flow problems are solved using the multipoint flux approximation (MPFA) method, and mechanics problems are solved using the multipoint stress approximation (MPSA) method. The coupled flow-mechanics problems share the same computational grid to avoid projection issues and allow for convenient exchange between them. We model the fracture mechanical behavior as a two-surface contact problem. The resulting coupled system of nonlinear equations is solved in a fully-implicit manner. The accuracy and generality of the numerical implementation are accessed using cases with analytical solutions, which shows an excellent match. We then apply the methodology to more complex cases to demonstrate its general applicability. We also investigate the geomechanical influence on fracture permeability change using 2D rock fractures. This work introduces a novel formulation for modeling the coupled flow-mechanics process in fractured reservoirs, and can be readily implemented in reservoir characterization workflow.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80395287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enterprise-Driven, Asset-Focused Digital Oilfield DOF Assessment - Strategic Framework and Roadmap","authors":"Elfadl Ibrahim, M. A. Al Hendi, Abdulla Al-Qamzi, Nasser A. Ballaith, Dr Esra Y. Al Hosani, S. Al Wehaibi, Omar I Al Hammadi, Muzahidin Muhamed Salim, I. Traboulay, Ghulam Shams, A. F. Ahmed, S. Ahmed, Rashi Dewan, Saltanat Yersaiyn","doi":"10.2118/207357-ms","DOIUrl":"https://doi.org/10.2118/207357-ms","url":null,"abstract":"\u0000 A new integrated growth strategy of an oil & gas company is focusing on maximizing the value of reserves and production in order to meet the value proposition of the highest possible return to the company. The strategy is built on the strategic foundation of the company of People, Performance, Profitability and Efficiency. From a business performance perspective, the strategy will bear fruit through increased production capacity, improved operational and cost efficiencies, re-energizing mature fields and uncovering new resources whilst maintaining safety and asset integrity.\u0000 The objective of this global level exercise aims to assess and evaluate various Digital Oilfield (DOF) practices and initiatives against industry best practices, to perform a landscape assessment of the upstream assets, to review the asset digital gap, to develop a strategic framework and roadmap ensuring that the company strategic pillars are supported across all relevant aspects, by closing the digital gap between current and future states. The assessment scope covers the following domains:\u0000 Reservoir management Production optimisation Operation management & integrity Engineering & projects Drilling Efficiency Logistics & Planning\u0000 The landscape assessment and gap analysis consist of several stages that starts from documenting the information received from the assets capturing their current business practices and processes, analyzing the \"as-is\" condition, designing the future state, assessing the impact to the specific assets, define the benefits and value and creating a 5-year business roadmap.\u0000 Aligned with the company DOF strategy, understanding the asset digital gap and enhancing the asset digital maturity will improve:\u0000 HSE and asset integrity by reducing hazard exposure, optimizing energy usage and improving wells and facilities integrity Collaboration and faster analysis leading to timely decision making Integrated operations by optimized drilling planning, operations, optimized production forecasting and integrated planning Optimum Reservoir Management through enhanced reservoir surveillance and recovery","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91234869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Drilling Data Quality Control Using Application of AI Technologies","authors":"F. Battocchio, Jaijith Sreekantan, A. Arnaout, A. Benaichouche, Juma Sulaiman Al Shamsi, Mohamad Abdul Salam Awad, Mohamed Ahmed Alnuaimi, Luis Ramon Baptista Peraza","doi":"10.2118/207598-ms","DOIUrl":"https://doi.org/10.2118/207598-ms","url":null,"abstract":"\u0000 Drilling data quality is notoriously a challenge for any analytics application, due to complexity of the real-time data acquisition system which routinely generates: (i) Time related issues caused by irregular sampling, (ii) Channel related issues in terms of non-uniform names and units, missing or wrong values, and (iii) Depth related issues caused block position resets, and depth compensation (for floating rigs). On the other hand, artificial intelligence drilling applications typically require a consistent stream of high-quality data as an input for their algorithms, as well as for visualization. In this work we present an automated workflow enhanced by data driven techniques that resolves complex quality issues, harmonize sensor drilling data, and report the quality of the dataset to be used for advanced analytics.\u0000 The approach proposes an automated data quality workflow which formalizes the characteristics, requirements and constraints of sensor data within the context of drilling operations. The workflow leverages machine learning algorithms, statistics, signal processing and rule-based engines for detection of data quality issues including error values, outliers, bias, drifts, noise, and missing values. Further, once data quality issues are classified, they are scored and treated on a context specific basis in order to recover the maximum volume of data while avoiding information loss. This results into a data quality and preparation engine that organizes drilling data for further advanced analytics, and reports the quality of the dataset through key performance indicators.\u0000 This novel data processing workflow allowed to recover more than 90% of a drilling dataset made of 18 offshore wells, that otherwise could not be used for analytics. This was achieved by resolving specific issues including, resampling timeseries with gaps and different sampling rates, smart imputation of wrong/missing data while preserving consistency of dataset across all channels. Additional improvement would include recovering data values that felt outside a meaningful range because of sensor drifting or depth resets.\u0000 The present work automates the end-to-end workflow for data quality control of drilling sensor data leveraging advanced Artificial Intelligence (AI) algorithms. It allows to detect and classify patterns of wrong/missing data, and to recover them through a context driven approach that prevents information loss. As a result, the maximum amount of data is recovered for artificial intelligence drilling applications. The workflow also enables optimal time synchronization of different sensors streaming data at different frequencies, within discontinuous time intervals.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"88 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91351318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Organizational Workforce by Successfully Facing and Overcoming Current Challenges","authors":"Maria Eugenia Yanez Banda, Francisco Javier Espina Gotera","doi":"10.2118/207487-ms","DOIUrl":"https://doi.org/10.2118/207487-ms","url":null,"abstract":"\u0000 During the year 2018, an operator evaluated the career paths and professional development of its technical personnel, extending its evaluation to how the contracting department acquired such training, which wasn't consistent with regard to a training provider, training topics, course agendas or training delivery.\u0000 Some of the main challenges in providing training are the engineers’ availability to attend classes and the scheduling constraints throughout the operator's five field locations. As an example, planning a class in the current organization might not reach the entire intended audience within the desired time frame because that class may only be scheduled in one of the five locations, and the engineers from the other four locations would need to travel to attend it. The impact of this may be that without the advantage of travelling to attend a specific training, the engineer may not be able to achieve all the promotional requirements for the next step in his/her career path.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"62 2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77125448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}