Over the next decade, attracting and retaining skilled workers will be one of the biggest challenges to oil and gas industry success. Talent management is a business strategy that organizations believe will enable them to retain their top talented employees and improve organization's performance. It is the process of effectively hiring the right talent, preparing them to take up top positions in future, assessing and managing their performance and also preventing them from leaving the organization. The performance of every organization depends on the performance of their employees. If the employees have unique competencies which the competitors cannot replicate, the organization automatically gains a competitive edge over its competitors. So, for managing this unique human capital, the organizations are focusing on creating effective systems and processes for talent management and the approaches to assessing current talent management efforts, identifying gaps and opportunities, and developing integrated action plans that is results-oriented and that can be aligned with each company's unique structure and long term goals. This paper highlights the positive relation between talent management and employee engagement, retention, value addition and improved organizational performance. The study reveals that there is positive association between these variables. This paper also verifies what the recent researchers thought about the positive impacts of talent management. The objectives of the study are to show the impact of Talent Management Techniques in the oil and gas industry on employee's engagement, motivation, loyalty, and performance. The present paper has been divided into four sections. In the first part, the changes occurring in the business environment and in particular the challenges faced by the oil and gas industry have been discussed. The second section explores the concept and reviews the available literature on talent management (TM). On the basis of review of literature the factors of talent management were identified and objectives and hypotheses were formulated. In the third section research framework and research methodology finds mention. The fourth section is about results and discussions. observations and conclusions are the harvest of different research methods used in the present study e.g. qualitative and quantitative surveys, interviews, workshops and discussion groups. Such methods are aiming at reaching verified and documented evidences of the actual findings derived from studying the impact of talent management on employees' performance and engagement. Talent management has become a hot topic everywhere. Review of Literature will explore different definitions, approaches and point of views regarding the Talent Management concepts and techniques. The paper links between findings of literature review and actual results and observations related to current oil and gas companies
{"title":"Implementing Talent Management and Its Effect on Employee Engagement and Organizational Performance","authors":"N. Kamel","doi":"10.2118/197665-ms","DOIUrl":"https://doi.org/10.2118/197665-ms","url":null,"abstract":"\u0000 \u0000 \u0000 Over the next decade, attracting and retaining skilled workers will be one of the biggest challenges to oil and gas industry success. Talent management is a business strategy that organizations believe will enable them to retain their top talented employees and improve organization's performance. It is the process of effectively hiring the right talent, preparing them to take up top positions in future, assessing and managing their performance and also preventing them from leaving the organization. The performance of every organization depends on the performance of their employees. If the employees have unique competencies which the competitors cannot replicate, the organization automatically gains a competitive edge over its competitors. So, for managing this unique human capital, the organizations are focusing on creating effective systems and processes for talent management and the approaches to assessing current talent management efforts, identifying gaps and opportunities, and developing integrated action plans that is results-oriented and that can be aligned with each company's unique structure and long term goals. This paper highlights the positive relation between talent management and employee engagement, retention, value addition and improved organizational performance. The study reveals that there is positive association between these variables. This paper also verifies what the recent researchers thought about the positive impacts of talent management. The objectives of the study are to show the impact of Talent Management Techniques in the oil and gas industry on employee's engagement, motivation, loyalty, and performance.\u0000 \u0000 \u0000 \u0000 The present paper has been divided into four sections. In the first part, the changes occurring in the business environment and in particular the challenges faced by the oil and gas industry have been discussed. The second section explores the concept and reviews the available literature on talent management (TM). On the basis of review of literature the factors of talent management were identified and objectives and hypotheses were formulated. In the third section research framework and research methodology finds mention. The fourth section is about results and discussions.\u0000 \u0000 \u0000 \u0000 observations and conclusions are the harvest of different research methods used in the present study e.g. qualitative and quantitative surveys, interviews, workshops and discussion groups. Such methods are aiming at reaching verified and documented evidences of the actual findings derived from studying the impact of talent management on employees' performance and engagement.\u0000 \u0000 \u0000 \u0000 Talent management has become a hot topic everywhere. Review of Literature will explore different definitions, approaches and point of views regarding the Talent Management concepts and techniques. The paper links between findings of literature review and actual results and observations related to current oil and gas companies\u0000","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81289983","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}
Luisa Caluri, Mirela Jianu, Paolo Cerioli, G. Silvestri
The purpose of this paper is to describe the process employed by Eni to enable digital innovation across the organization. In particular, it focuses on illustrating the reinforcing role played by the unit dedicated to the open innovation initiatives in identifying the most promising digital solutions and innovative providers. The present paper analyzes the case of Eni, one of the biggest oil companies in the world and among the first esatblishers of a Digital Unit that reports directly to the CEO, based on an internal study using the qualitative methodology. A description of processes for development / execution of open innovation initiatives is provided, alongside a view on the main stakeholders involved that make this approach a value creation driver. The paper tackles also elements of novelty and efficiency regarding both the organizational (structural) aspects and the work, as well as information flows within the company and across the departments involved. Ultimately, this case study is meant to illustrate the main features and success factors that made Eni Digital Open Innovation Unit effective in promoting an organizational and cultural development and by this supporting the digital transformation and encouraging corporate dynamism. Valuable insights can be therefore drawn on how organizational and procedural decisions, adoption of new instruments and ways of working through digital empowerment and open innovation philosophy dissiminated by the Digital Department can boost the attainable results throughout the organization.
{"title":"Open Innovation as Enabling Paradigm to Empower Digital Transformation in Oil & Gas Organizations","authors":"Luisa Caluri, Mirela Jianu, Paolo Cerioli, G. Silvestri","doi":"10.2118/197904-ms","DOIUrl":"https://doi.org/10.2118/197904-ms","url":null,"abstract":"\u0000 The purpose of this paper is to describe the process employed by Eni to enable digital innovation across the organization. In particular, it focuses on illustrating the reinforcing role played by the unit dedicated to the open innovation initiatives in identifying the most promising digital solutions and innovative providers.\u0000 The present paper analyzes the case of Eni, one of the biggest oil companies in the world and among the first esatblishers of a Digital Unit that reports directly to the CEO, based on an internal study using the qualitative methodology.\u0000 A description of processes for development / execution of open innovation initiatives is provided, alongside a view on the main stakeholders involved that make this approach a value creation driver. The paper tackles also elements of novelty and efficiency regarding both the organizational (structural) aspects and the work, as well as information flows within the company and across the departments involved.\u0000 Ultimately, this case study is meant to illustrate the main features and success factors that made Eni Digital Open Innovation Unit effective in promoting an organizational and cultural development and by this supporting the digital transformation and encouraging corporate dynamism. Valuable insights can be therefore drawn on how organizational and procedural decisions, adoption of new instruments and ways of working through digital empowerment and open innovation philosophy dissiminated by the Digital Department can boost the attainable results throughout the organization.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89644696","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}
A. Salomone, S. Burrafato, G. R. Maccarini, R. Poloni, Valeriano Gioia, A. Concas, G. Tangen, Arve Huse, Lucio Antoniani, Mats Andersen, Sanna Zainoune
This paper presents the positive results of the first deployment of wired drill pipe (WDP) technology and along-string measurement (ASM) tools in drilling operations in the Adriatic Sea. The WDP system was used within the frame of a multi-objective testing program, in conjunction with an experimental downhole tool. The system allowed transmission of real-time, high-density, low-latency data from logging-while- drilling (LWD) tools and from ASM subs. These tools provided temperature, annular/internal pressure, rotation, and vibration data. This was the first time WDP and ASM tools were used by an operator in the Adriatic Sea. The system was also used for activation and communication with another experimental downhole tool on this project. The high-speed telemetry system made it possible to achieve impressive operational and performance benefits. Annular pressure measured along the string provided a better understanding of the drilling mud condition and behavior along the wellbore, thereby allowing the operator to stay in the safe mud-weight window and helping them to avoid unintentional hole fractures or collapse. During pumping in and out of hole, swab and surge were also monitored closely with downhole, real- time measurements from the ASM tools. The same effects were controlled after drilling each stand, when the interval drilled was reamed to ensure sufficient hole cleaning. While drilling, the system raised the rate of penetration (ROP) limit by removing constraints on data acquisition while still providing the confidence that the hole was being cleaned while drilling. Drillstring vibration was recorded as well, and potential benefit in preventing premature failure of downhole tools were highlighted. The test verified that improved drilling performance was enabled using WDP technology. Awareness of downhole conditions and a substantial reduction in risk were also benefits. In addition, the technology unlocked bidrectional communication and control with modern downhole tools.
{"title":"First Wired Drill Pipe Deployment in Adriatic Sea","authors":"A. Salomone, S. Burrafato, G. R. Maccarini, R. Poloni, Valeriano Gioia, A. Concas, G. Tangen, Arve Huse, Lucio Antoniani, Mats Andersen, Sanna Zainoune","doi":"10.2118/197833-ms","DOIUrl":"https://doi.org/10.2118/197833-ms","url":null,"abstract":"\u0000 This paper presents the positive results of the first deployment of wired drill pipe (WDP) technology and along-string measurement (ASM) tools in drilling operations in the Adriatic Sea. The WDP system was used within the frame of a multi-objective testing program, in conjunction with an experimental downhole tool.\u0000 The system allowed transmission of real-time, high-density, low-latency data from logging-while- drilling (LWD) tools and from ASM subs. These tools provided temperature, annular/internal pressure, rotation, and vibration data. This was the first time WDP and ASM tools were used by an operator in the Adriatic Sea. The system was also used for activation and communication with another experimental downhole tool on this project.\u0000 The high-speed telemetry system made it possible to achieve impressive operational and performance benefits. Annular pressure measured along the string provided a better understanding of the drilling mud condition and behavior along the wellbore, thereby allowing the operator to stay in the safe mud-weight window and helping them to avoid unintentional hole fractures or collapse.\u0000 During pumping in and out of hole, swab and surge were also monitored closely with downhole, real- time measurements from the ASM tools. The same effects were controlled after drilling each stand, when the interval drilled was reamed to ensure sufficient hole cleaning.\u0000 While drilling, the system raised the rate of penetration (ROP) limit by removing constraints on data acquisition while still providing the confidence that the hole was being cleaned while drilling. Drillstring vibration was recorded as well, and potential benefit in preventing premature failure of downhole tools were highlighted.\u0000 The test verified that improved drilling performance was enabled using WDP technology. Awareness of downhole conditions and a substantial reduction in risk were also benefits. In addition, the technology unlocked bidrectional communication and control with modern downhole tools.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84908876","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}
P. Agnihotri, V. Pandey, P. Thakur, M. A. Mansoori, M. Rebelle, Steve Smith, Pranjal Bhatt, G. Zhunussova, S. Hassan
Holistic assessment of project economics and subsurface characterization provides a framework to handle challenging reservoirs. Capturing ranked uncertainties based on their impact on the project and meticulous working towards de-risking the project is key for the success of the entire project. Committing increased production from the field is dependent on proper evaluation of the reservoir. This paper reviews characterization of a tight reservoir deposited in the intra-shelf Bab basin during lower Aptian time. Initial stage reservoir characterization is critical in formulating reservoir development plan and estimating a realistic assessment of rates and volumes for the field. The target formation is a low-permeability (average permeability 0.5 mD) heterogeneous carbonate reservoir sitting directly above and adjacent to a producing carbonate reservoir. It is essential to understand communication between the zones. The pilot well is drilled with 225 ft of conventional core and quad-combo logs. Advanced logs such as resistivity image, cross-dipole acoustic, nuclear magnetic resonance, vertical interference test (VIT), formation pressure (including pressure transient data), and fluid samples were acquired. The main objectives of the evaluation program were to determine the formation pressure, collect representative oil sample(s), conduct vertical interference tests between the sub-zones and collect appropriate data for geomechanical and rock-physics characterization. Thorough pre-job planning and cross-discipline cooperation during the operation provided high fidelity log data and interpretation of the data into a coherent result. This included integration of image data with vertical interference tests from the wireline formation tester (WFT) where barriers were confirmed. In addition, NMR permeability was matched and calibrated using pretest mobility measurements and formation pressure data was combined with full waveform advanced acoustic processing to explain the communication between the upper target zone and the lower producing reservoir. Advanced acoustic analysis helped to fully characterize the target formations with stoneley permeability, azimuthal anisotropy, and presence of fractures. This paper demonstrates the importance of multi-disciplinary team effort in characterization of challenging reservoirs. It highlights the importance of holistic planning before the execution phase, and keeping a focus on the larger goal while executing individual aspect of a complicated project. Formation evaluation measurements have evolved over decades and occasionally it benefits the industry to provide a review of how the latest logging measurements fit together in an integrated manner, for successful evaluation of a challenging reservoir.
{"title":"Static and Dynamic Characterization of a Tight Reservoir- A Case-Study from Onshore Abu Dhabi Field","authors":"P. Agnihotri, V. Pandey, P. Thakur, M. A. Mansoori, M. Rebelle, Steve Smith, Pranjal Bhatt, G. Zhunussova, S. Hassan","doi":"10.2118/197721-ms","DOIUrl":"https://doi.org/10.2118/197721-ms","url":null,"abstract":"\u0000 Holistic assessment of project economics and subsurface characterization provides a framework to handle challenging reservoirs. Capturing ranked uncertainties based on their impact on the project and meticulous working towards de-risking the project is key for the success of the entire project. Committing increased production from the field is dependent on proper evaluation of the reservoir.\u0000 This paper reviews characterization of a tight reservoir deposited in the intra-shelf Bab basin during lower Aptian time. Initial stage reservoir characterization is critical in formulating reservoir development plan and estimating a realistic assessment of rates and volumes for the field.\u0000 The target formation is a low-permeability (average permeability 0.5 mD) heterogeneous carbonate reservoir sitting directly above and adjacent to a producing carbonate reservoir. It is essential to understand communication between the zones. The pilot well is drilled with 225 ft of conventional core and quad-combo logs. Advanced logs such as resistivity image, cross-dipole acoustic, nuclear magnetic resonance, vertical interference test (VIT), formation pressure (including pressure transient data), and fluid samples were acquired. The main objectives of the evaluation program were to determine the formation pressure, collect representative oil sample(s), conduct vertical interference tests between the sub-zones and collect appropriate data for geomechanical and rock-physics characterization.\u0000 Thorough pre-job planning and cross-discipline cooperation during the operation provided high fidelity log data and interpretation of the data into a coherent result. This included integration of image data with vertical interference tests from the wireline formation tester (WFT) where barriers were confirmed. In addition, NMR permeability was matched and calibrated using pretest mobility measurements and formation pressure data was combined with full waveform advanced acoustic processing to explain the communication between the upper target zone and the lower producing reservoir. Advanced acoustic analysis helped to fully characterize the target formations with stoneley permeability, azimuthal anisotropy, and presence of fractures.\u0000 This paper demonstrates the importance of multi-disciplinary team effort in characterization of challenging reservoirs. It highlights the importance of holistic planning before the execution phase, and keeping a focus on the larger goal while executing individual aspect of a complicated project.\u0000 Formation evaluation measurements have evolved over decades and occasionally it benefits the industry to provide a review of how the latest logging measurements fit together in an integrated manner, for successful evaluation of a challenging reservoir.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83338458","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}
Pedro A. Romero Rojas, Manuel M. Rincón, P. Netto, Bernardo Coutinho
Fluid typing, meaning fluid identification and quantification of each phase, is a significant challenge in NMR data postprocessing, particularly when the fluid spectral distributions overlap in one-dimension (T1, T2 spectra) or two dimensions (T1T2 or DT2 maps). Typical examples are extra-heavy oil and clay-bound water (CBW), heavy oil and capillary-bound water (BVI), free water and light oil or light oil-water and oil-base mud filtrate (OBMF). In these cases, technical limitations in data acquisitions and constraints in the inversion algorithms result in poor spectral resolution for those fluids with very similar physical-chemical properties. This makes very difficult the interpretation of NMR measurements from the laboratory as well downhole. We present two case studies: one focused on determining water saturation (Sw) in core samples in a water-oil displacement experiment in the laboratory; the second is about determining the permeability by identifying OBMF from an NMR well log in a medium to light oil-bearing formation. In both cases the targeted fluid component was determined using blind source separation based on independent component analysis (BSS-ICA), which is a machine learning tool capable of separating spectral T2 components (sources) given their statistical independency. The results from the displacement experimental show a high correlation (R2 higher than .85) between saturation from the BSS-ICA derived water component and the estimated value from known injected water volumes. In the well log case, the results show that the presence of OBMF and its volume are a good indicator of rock quality of the reservoir layers, as confirmed from several core measurements. Beyond this, the OBMF obtained from BSS-ICA decomposition is used as a key variable in a newly proposed permeability equation. After core calibration the OBMF-based permeability was found to be more representative than the permeability derived from the widely used Timur-Coates equation.
{"title":"Application of Machine Learning Tool to Separate Overlapping Fluid Components on NMR T2 Distributions: Case Studies from Laboratory Displacement Experiment and Well Logs","authors":"Pedro A. Romero Rojas, Manuel M. Rincón, P. Netto, Bernardo Coutinho","doi":"10.2118/197684-ms","DOIUrl":"https://doi.org/10.2118/197684-ms","url":null,"abstract":"\u0000 Fluid typing, meaning fluid identification and quantification of each phase, is a significant challenge in NMR data postprocessing, particularly when the fluid spectral distributions overlap in one-dimension (T1, T2 spectra) or two dimensions (T1T2 or DT2 maps). Typical examples are extra-heavy oil and clay-bound water (CBW), heavy oil and capillary-bound water (BVI), free water and light oil or light oil-water and oil-base mud filtrate (OBMF). In these cases, technical limitations in data acquisitions and constraints in the inversion algorithms result in poor spectral resolution for those fluids with very similar physical-chemical properties. This makes very difficult the interpretation of NMR measurements from the laboratory as well downhole.\u0000 We present two case studies: one focused on determining water saturation (Sw) in core samples in a water-oil displacement experiment in the laboratory; the second is about determining the permeability by identifying OBMF from an NMR well log in a medium to light oil-bearing formation. In both cases the targeted fluid component was determined using blind source separation based on independent component analysis (BSS-ICA), which is a machine learning tool capable of separating spectral T2 components (sources) given their statistical independency.\u0000 The results from the displacement experimental show a high correlation (R2 higher than .85) between saturation from the BSS-ICA derived water component and the estimated value from known injected water volumes. In the well log case, the results show that the presence of OBMF and its volume are a good indicator of rock quality of the reservoir layers, as confirmed from several core measurements. Beyond this, the OBMF obtained from BSS-ICA decomposition is used as a key variable in a newly proposed permeability equation. After core calibration the OBMF-based permeability was found to be more representative than the permeability derived from the widely used Timur-Coates equation.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78457407","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}
The Pengerang Integrated Complex (PIC) is a US$27b multi package fast-tracked mega project with execution period of 60 months to ‘Ready for Start-up’ in Q1 2019. One of the largest project of its kind in the world constructed at a single time and location, PIC consists of a Refinery and Petrochemical Integrated Development (RAPID) and supporting facilities. A successful integrated system schedule was established towards achieving on-time project completion by early 2019. The key challenges lie in the immense complexity of the project with involvement of over 140 organizations including more than 40 EPCC packages, supported by 13 licensors and over 200 suppliers and Contractors, around 250k EPCC schedule line items and alignment of 117 Integrated Networks across over 30 main process units. To drive a successful system schedule integration, three key guiding principles were followed: a pre-emptive schedule management guideline for systemization and start-up sequence, integrated critical path schedule alignment engagements with EPCC Contractors and a lean task force collaboration by project planning and operation on prioritized critical utilities delivery for EPCC commissioning activities. As a result of the successful system schedule alignment and integration, the critical utilities i.e. power, steam, cooling water, fuel gas and fire water, were successfully delivered in quick successions according to the respective EPCC packages needs, allowing commissioning activities within the packages and the Integrated Networks crossing over multiple EPCC packages. The cohesive pre-commissioning, commissioning and start-up schedule between all EPCC packages propelled PIC project to a momentous "Finish Strong". The critical success factor comprising of early creation of special task force driven by project planning and operation personnel in managing system schedule prioritization and strong collaboration with EPCC Contractors, maybe the answer to future projects looking for integrated smart solutions.
{"title":"Successful System Schedule Integration for a Complex Multi Package Mega Project","authors":"Sahaiza Saat","doi":"10.2118/197376-ms","DOIUrl":"https://doi.org/10.2118/197376-ms","url":null,"abstract":"\u0000 The Pengerang Integrated Complex (PIC) is a US$27b multi package fast-tracked mega project with execution period of 60 months to ‘Ready for Start-up’ in Q1 2019. One of the largest project of its kind in the world constructed at a single time and location, PIC consists of a Refinery and Petrochemical Integrated Development (RAPID) and supporting facilities. A successful integrated system schedule was established towards achieving on-time project completion by early 2019.\u0000 The key challenges lie in the immense complexity of the project with involvement of over 140 organizations including more than 40 EPCC packages, supported by 13 licensors and over 200 suppliers and Contractors, around 250k EPCC schedule line items and alignment of 117 Integrated Networks across over 30 main process units. To drive a successful system schedule integration, three key guiding principles were followed: a pre-emptive schedule management guideline for systemization and start-up sequence, integrated critical path schedule alignment engagements with EPCC Contractors and a lean task force collaboration by project planning and operation on prioritized critical utilities delivery for EPCC commissioning activities.\u0000 As a result of the successful system schedule alignment and integration, the critical utilities i.e. power, steam, cooling water, fuel gas and fire water, were successfully delivered in quick successions according to the respective EPCC packages needs, allowing commissioning activities within the packages and the Integrated Networks crossing over multiple EPCC packages. The cohesive pre-commissioning, commissioning and start-up schedule between all EPCC packages propelled PIC project to a momentous \"Finish Strong\".\u0000 The critical success factor comprising of early creation of special task force driven by project planning and operation personnel in managing system schedule prioritization and strong collaboration with EPCC Contractors, maybe the answer to future projects looking for integrated smart solutions.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76047625","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}
S. Masalmeh, A. Alsumaiti, N. Gaillard, F. Daguerre, T. Skauge, Arne Skuage
Polymer flooding is a mature EOR technique successfully applied in both sandstone and carbonate reservoirs. ADNOC has developed a new EOR roadmap with the objective to identify and mature EOR options to improve displacement and sweep efficiency in carbonate reservoirs. Polymer based EOR was identified as one of the main EOR options. These options include polymer injection, simultaneous injection of miscible gas and polymer (SIMGAP), simultaneous injection of water and polymer (SIWAP), low salinity polymer, etc. However, the conditions of the reservoirs in Abu Dhabi are beyond the industry experience for the application of polymer which pose a significant challenge to polymer based EOR processes. The reservoirs are at high temperature (~100-130 °C), the formation brine is of high salinity (~200,000 ppm), the brine also has high concentrations of divalent ions (~18,000 ppm of Ca++ and Mg++) and the reservoir formation is carbonate where there is little experience in the industry for polymer injection. The stability of polymers is known to be severely affected at such conditions of high temperature, salinity and divalent ions concentration. Therefore, the main challenge for polymer based EOR processes in ADNOC reservoirs (such as polymer flooding, SIMGAP, SIWAP, etc.) is to find a polymer that are stable under such extreme conditions and can be injected in carbonate reservoirs. ADNOC has lunched a number of studies to experimentally investigate 1- the stability of polymers at such adverse conditions of high salinity, high temperature and high divalent ions, 2- injectivity of polymers in carbonate rocks using both outcrop and reservoir core plugs, 3- bulk and in-situ rheology of polymers, 4- effects of various parameters on the polymer performance, such as polymer concentration, shear rate and presence of oil on polymer retention and in-situ rheology and 5- impact of H2S and Oxygen on polymer stability. In this paper, we report the results of these different studies that can enlarge the application envelope of polymer flooding to high-temperature, high-salinity and light oil Middle Eastern carbonate reservoirs. The main conclusions of the studies are: 1- A polyacrylamide based polymer with high content of ATBS (SAV 10) was identified as stable at Abu Dhabi reservoir conditions, 2- SAV 10 polymer is also stable in the presence of H2S (500 ppm) and/or Oxygen up to 150 ppb, 3- The polymer has good injectivity in a wide range of injection rates ranging from 1ft/day to 120ft/day and wide range of permeability, 4- The polymer showed a shear thickening behavior with an increase in flux without any signs of mechanical degradation, noted by the stable viscosities of the effluents, 6- The presence of crude oil had significant impact on injectivity, in-situ rheology and adsorption in carbonate core material. In summary, the ATBS polymer showed a promising injectivity behavior which can be modulated for injection in the target reservoirs. In addition, as th
{"title":"Extending Polymer Flooding Towards High-Temperature and High-Salinity Carbonate Reservoirs","authors":"S. Masalmeh, A. Alsumaiti, N. Gaillard, F. Daguerre, T. Skauge, Arne Skuage","doi":"10.2118/197647-ms","DOIUrl":"https://doi.org/10.2118/197647-ms","url":null,"abstract":"\u0000 Polymer flooding is a mature EOR technique successfully applied in both sandstone and carbonate reservoirs. ADNOC has developed a new EOR roadmap with the objective to identify and mature EOR options to improve displacement and sweep efficiency in carbonate reservoirs. Polymer based EOR was identified as one of the main EOR options. These options include polymer injection, simultaneous injection of miscible gas and polymer (SIMGAP), simultaneous injection of water and polymer (SIWAP), low salinity polymer, etc. However, the conditions of the reservoirs in Abu Dhabi are beyond the industry experience for the application of polymer which pose a significant challenge to polymer based EOR processes. The reservoirs are at high temperature (~100-130 °C), the formation brine is of high salinity (~200,000 ppm), the brine also has high concentrations of divalent ions (~18,000 ppm of Ca++ and Mg++) and the reservoir formation is carbonate where there is little experience in the industry for polymer injection. The stability of polymers is known to be severely affected at such conditions of high temperature, salinity and divalent ions concentration. Therefore, the main challenge for polymer based EOR processes in ADNOC reservoirs (such as polymer flooding, SIMGAP, SIWAP, etc.) is to find a polymer that are stable under such extreme conditions and can be injected in carbonate reservoirs.\u0000 ADNOC has lunched a number of studies to experimentally investigate 1- the stability of polymers at such adverse conditions of high salinity, high temperature and high divalent ions, 2- injectivity of polymers in carbonate rocks using both outcrop and reservoir core plugs, 3- bulk and in-situ rheology of polymers, 4- effects of various parameters on the polymer performance, such as polymer concentration, shear rate and presence of oil on polymer retention and in-situ rheology and 5- impact of H2S and Oxygen on polymer stability.\u0000 In this paper, we report the results of these different studies that can enlarge the application envelope of polymer flooding to high-temperature, high-salinity and light oil Middle Eastern carbonate reservoirs. The main conclusions of the studies are: 1- A polyacrylamide based polymer with high content of ATBS (SAV 10) was identified as stable at Abu Dhabi reservoir conditions, 2- SAV 10 polymer is also stable in the presence of H2S (500 ppm) and/or Oxygen up to 150 ppb, 3- The polymer has good injectivity in a wide range of injection rates ranging from 1ft/day to 120ft/day and wide range of permeability, 4- The polymer showed a shear thickening behavior with an increase in flux without any signs of mechanical degradation, noted by the stable viscosities of the effluents, 6- The presence of crude oil had significant impact on injectivity, in-situ rheology and adsorption in carbonate core material.\u0000 In summary, the ATBS polymer showed a promising injectivity behavior which can be modulated for injection in the target reservoirs. In addition, as th","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78920392","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}
Downhole vibration remains a major challenge for drillers. Today, there is technology to look at the problem from a unique perspective. A novel look at the problem focuses on evaluation of machine learning algorithms to predict downhole vibrations. Prediction is the first step in a longer road map. The goal would be to find an optimal combination of revolutions per minute (RPM) and weight-on-bit (WOB) to remedy drilling vibration in real-time, hence closing the loop. Drilling mechanics data for thousands of wells, acquired over more than ten years was analyzed. Some preparation of the drilling mechanics data was required. Data cleaning was first performed. This included corrections for time-dependent nature of the data. Data imputing for missing values and handling of outliers and anomalies was also performed in this stage. This was followed by feature engineering which included adding variables based on company-wide drilling domain expertise. Variables to capture data patterns and variables for better capturing the time-series dependencies were also created in this stage. This paper will discuss methodologies and general rules that were tested for preparing unstructured drilling data. A few of the machine learning algorithms used as building blocks of our full solution are gradient boosting and random forest. Deep learning models were also tested and the value of these are compared. The results were compiled to decide the best algorithm which could further be used to fine-tune optimum performance. The time series aspect of the data is captured in a moving window. As the window increases, the performance of each algorithm also varied. Also, evaluation of the benefits and drawbacks of each algorithm for the drilling predictions is detailed. Ways to improve the accuracy of prediction for downhole vibrations is also suggested with reference to the results showing the logic behind all recommendations. There will be a summary of the details of each finding and a short discussion on the way forward for the industry.
{"title":"Machine Learning Lessons Learnt in Stick-Slip Prediction","authors":"Soumya Gupta, Crispin Chatar, J. Celaya","doi":"10.2118/197584-ms","DOIUrl":"https://doi.org/10.2118/197584-ms","url":null,"abstract":"\u0000 Downhole vibration remains a major challenge for drillers. Today, there is technology to look at the problem from a unique perspective. A novel look at the problem focuses on evaluation of machine learning algorithms to predict downhole vibrations. Prediction is the first step in a longer road map. The goal would be to find an optimal combination of revolutions per minute (RPM) and weight-on-bit (WOB) to remedy drilling vibration in real-time, hence closing the loop. Drilling mechanics data for thousands of wells, acquired over more than ten years was analyzed. Some preparation of the drilling mechanics data was required. Data cleaning was first performed. This included corrections for time-dependent nature of the data. Data imputing for missing values and handling of outliers and anomalies was also performed in this stage. This was followed by feature engineering which included adding variables based on company-wide drilling domain expertise. Variables to capture data patterns and variables for better capturing the time-series dependencies were also created in this stage.\u0000 This paper will discuss methodologies and general rules that were tested for preparing unstructured drilling data. A few of the machine learning algorithms used as building blocks of our full solution are gradient boosting and random forest. Deep learning models were also tested and the value of these are compared. The results were compiled to decide the best algorithm which could further be used to fine-tune optimum performance. The time series aspect of the data is captured in a moving window. As the window increases, the performance of each algorithm also varied. Also, evaluation of the benefits and drawbacks of each algorithm for the drilling predictions is detailed. Ways to improve the accuracy of prediction for downhole vibrations is also suggested with reference to the results showing the logic behind all recommendations. There will be a summary of the details of each finding and a short discussion on the way forward for the industry.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"229 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77588303","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}
Cunliang Chen, Ming Yang, Xiaodong Han, Jianbo Zhang
Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods. This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance. This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy. By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.
{"title":"Water Flooding Performance Prediction in Layered Reservoir Using Big Data and Artificial Intelligence Algorithms","authors":"Cunliang Chen, Ming Yang, Xiaodong Han, Jianbo Zhang","doi":"10.2118/197585-ms","DOIUrl":"https://doi.org/10.2118/197585-ms","url":null,"abstract":"\u0000 Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods.\u0000 This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance.\u0000 This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy.\u0000 By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74786997","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}
E. Behm, Mohammed Al Asimi, Sara Al Maskari, Wladimir Juna, H. Klie, Duc Le, G. Lutidze, R. Rastegar, A. Reynolds, Vinit Tathed, R. Younis, Yuchen Zhang
Occidental Mukhaizna completed a steamflood field optimization demonstration project involving about 100 Mukhaizna wells from Mid-December 2018 to Mid-March 2019. The field demonstration involves a data analytics process that provides recommendations on the best steam injection allocation among wells in order to improve overall steamflood performance. The process uses a low fidelity physics-based proxy model and cloud-based parallel processing. A field optimization engineer history matches and anchors a proxy model to current well and field operating constraints. The engineer completes hundreds of forward runs as part of an optimization algorithm to identify scenarios most likely to help increase value (oil production per steam injected) over the short term in the field, while honoring all producing and injection well operating ranges. The reservoir management team vets the rate change ideas generated and provides their recommendations for changes so the likely best and most practical overall scenario is implemented. The process is refreshed monthly so field performance results are included immediately, and the optimization process is kept evergreen. The field results so far have been encouraging, yielding an increase in oil production that has exceeded expectations. This paper will describe the data analytics field optimization process and workflow, present the baseline performance versus field demonstration results, and share lessons learned.
{"title":"Middle East Steamflood Field Optimization Demonstration Project","authors":"E. Behm, Mohammed Al Asimi, Sara Al Maskari, Wladimir Juna, H. Klie, Duc Le, G. Lutidze, R. Rastegar, A. Reynolds, Vinit Tathed, R. Younis, Yuchen Zhang","doi":"10.2118/197751-ms","DOIUrl":"https://doi.org/10.2118/197751-ms","url":null,"abstract":"\u0000 Occidental Mukhaizna completed a steamflood field optimization demonstration project involving about 100 Mukhaizna wells from Mid-December 2018 to Mid-March 2019.\u0000 The field demonstration involves a data analytics process that provides recommendations on the best steam injection allocation among wells in order to improve overall steamflood performance. The process uses a low fidelity physics-based proxy model and cloud-based parallel processing. A field optimization engineer history matches and anchors a proxy model to current well and field operating constraints. The engineer completes hundreds of forward runs as part of an optimization algorithm to identify scenarios most likely to help increase value (oil production per steam injected) over the short term in the field, while honoring all producing and injection well operating ranges. The reservoir management team vets the rate change ideas generated and provides their recommendations for changes so the likely best and most practical overall scenario is implemented. The process is refreshed monthly so field performance results are included immediately, and the optimization process is kept evergreen. The field results so far have been encouraging, yielding an increase in oil production that has exceeded expectations.\u0000 This paper will describe the data analytics field optimization process and workflow, present the baseline performance versus field demonstration results, and share lessons learned.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"61 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85224819","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}
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