Storage of CO2 in depleted gas reservoirs or large aquifers is one of the available solutions to reduce anthropogenic greenhouse gas emissions. Numerical modeling of these processes requires the use of large geological models with several orders of magnitude of variations in the porous media properties. Moreover, modeling the injection of highly concentrated and cold CO2 in large reservoirs with the correct physics is introducing numerical challenges that conventional reservoir simulators cannot handle. We propose a thermal formulation based on a full equation of state formalism to model pure CO2 and CO2 mixtures with the residual gas of depleted reservoirs. Most of the reservoir simulators model the phase-equilibriums with a pressure-temperature based formulation. With this usual framework, it is not possible to exhibit two phases with pure CO2 contents. Moreover, in this classical framework, the crossing of the phase envelope is associated with a large discontinuity in the enthalpy computation which can prevent the convergence of the energy conservation equation. In this work, accurate and continuous phase properties are obtained basing our formulation on enthalpy as a primary variable. We first implement a new phase-split algorithm with input variables as pressure and enthalpy instead of the usual pressure and temperature and we validate it on several test cases. This algorithm can model situations where the mixture can change rapidly from one phase to the other at constant pressure and temperature. Then treating enthalpy instead of temperature as a primary variable in both the reservoir and the well modeling algorithms, our reservoir simulator can model situations with pure or near pure components as well as crossing of the phase envelope that usual formulations implemented in reservoir simulators cannot handle. We first validate our new formulation against the usual formulation on a problem where both formulations can correctly represent the physics. Then we show situations where the usual formulations fail to represent the correct physics and that are simulated well with our new formulation. Finally, we apply our new model for the simulation of pure and cold CO2 injection in a real depleted gas reservoir from the Netherlands.
{"title":"Complete EOS Thermal Formulation for Simulation of CO2 Storage","authors":"A. Moncorgé, M. Petitfrère, S. Thibeau","doi":"10.2118/205447-ms","DOIUrl":"https://doi.org/10.2118/205447-ms","url":null,"abstract":"\u0000 Storage of CO2 in depleted gas reservoirs or large aquifers is one of the available solutions to reduce anthropogenic greenhouse gas emissions. Numerical modeling of these processes requires the use of large geological models with several orders of magnitude of variations in the porous media properties. Moreover, modeling the injection of highly concentrated and cold CO2 in large reservoirs with the correct physics is introducing numerical challenges that conventional reservoir simulators cannot handle. We propose a thermal formulation based on a full equation of state formalism to model pure CO2 and CO2 mixtures with the residual gas of depleted reservoirs. Most of the reservoir simulators model the phase-equilibriums with a pressure-temperature based formulation. With this usual framework, it is not possible to exhibit two phases with pure CO2 contents. Moreover, in this classical framework, the crossing of the phase envelope is associated with a large discontinuity in the enthalpy computation which can prevent the convergence of the energy conservation equation. In this work, accurate and continuous phase properties are obtained basing our formulation on enthalpy as a primary variable. We first implement a new phase-split algorithm with input variables as pressure and enthalpy instead of the usual pressure and temperature and we validate it on several test cases. This algorithm can model situations where the mixture can change rapidly from one phase to the other at constant pressure and temperature. Then treating enthalpy instead of temperature as a primary variable in both the reservoir and the well modeling algorithms, our reservoir simulator can model situations with pure or near pure components as well as crossing of the phase envelope that usual formulations implemented in reservoir simulators cannot handle. We first validate our new formulation against the usual formulation on a problem where both formulations can correctly represent the physics. Then we show situations where the usual formulations fail to represent the correct physics and that are simulated well with our new formulation. Finally, we apply our new model for the simulation of pure and cold CO2 injection in a real depleted gas reservoir from the Netherlands.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129929672","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}
J. Pimentel, Robin Slater, Andrew Grant, Rune Vesterkjaer, T. Normann, Rajeev Kothari, J. Sandberg
This paper proposes a road map for the integration of renewable energy supply to power subsea processing systems. To replace the traditional power supply, like fossil fuel-based generators or grid power, a wind turbine generator (WTG) operating on a islanded mode has been introduced and discussed. A review of the state of the art of WTGs is performed, primarily focused on power and controls aspects, with identification of the main technological gaps left to achieve wind-powered subsea processing. To fully assess the renewable energy integration and current gaps, a study case is proposed which addresses a subsea compression train powered by offshore wind. A thorough analysis is conducted, with meteorological conditions based on the NCS (Norwegian Continental Shelf), where gas line packing is proposed as an innovative means of energy storage. Finally, an economic analysis as well as a CO2 emission estimate is presented to demonstrate the benefits of the proposed road map. Some further discussions and conclusions are presented as well as some propositions for future works.
{"title":"A Road Map for Renewable Energy Integration with Subsea Processing Systems","authors":"J. Pimentel, Robin Slater, Andrew Grant, Rune Vesterkjaer, T. Normann, Rajeev Kothari, J. Sandberg","doi":"10.2118/205433-ms","DOIUrl":"https://doi.org/10.2118/205433-ms","url":null,"abstract":"\u0000 This paper proposes a road map for the integration of renewable energy supply to power subsea processing systems. To replace the traditional power supply, like fossil fuel-based generators or grid power, a wind turbine generator (WTG) operating on a islanded mode has been introduced and discussed. A review of the state of the art of WTGs is performed, primarily focused on power and controls aspects, with identification of the main technological gaps left to achieve wind-powered subsea processing. To fully assess the renewable energy integration and current gaps, a study case is proposed which addresses a subsea compression train powered by offshore wind. A thorough analysis is conducted, with meteorological conditions based on the NCS (Norwegian Continental Shelf), where gas line packing is proposed as an innovative means of energy storage. Finally, an economic analysis as well as a CO2 emission estimate is presented to demonstrate the benefits of the proposed road map. Some further discussions and conclusions are presented as well as some propositions for future works.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128361490","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}
Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods. An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity. Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation. Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations. The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries. Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.
{"title":"Real Time Cloud-Based Automation for Formation Evaluation Optimization, Risk Mitigation and Decarbonization","authors":"R. Nye, C. Mejia, Evgeniya Dontsova","doi":"10.2118/205402-ms","DOIUrl":"https://doi.org/10.2118/205402-ms","url":null,"abstract":"\u0000 Recent developments in artificial intelligence (AI) have enabled upstream exploration and production companies to make better, faster and accurate decisions at any stage of well construction, while reducing operational expenditure and risk, increasing logistic efficiencies. The achieved optimization through digitization at the wellsite will significantly reduce the carbon emissions per well drilled when fully embraced by the industry. In addition, an industry pushed to drill in more challenging environments, they must embrace safer and more practical methods.\u0000 An increase in prediction techniques, to generate synthetic formation evaluation wellbore logs, has unlocked the ability to implement a combination of predictive and prescriptive analytics with petrophysical and geochemical workflows in real time. The foundation of the real time automation is based on advanced machine learning (ML) techniques that are deployed via cloud connectivity.\u0000 Three levels of logging precision are defined in the automated workflow based on the data inputs and machine learning models. The first level is the forecasting ahead of the bit that implements advanced machine learning using historical data, aiding proactive operational decisions. The second level has improved precision by incorporating real time drilling measurements and providing a credible contingency to for wellbore logging program. The last level incorporates petrophysical workflows and geochemical measurements to achieve the highest precision for logging prediction in the industry. Supervised and unsupervised machine learning models are presented to demonstrate the path for automation.\u0000 Precision above 95% in the real time automated workflows was achieved with a combination of physics and advanced machine learning models. The automation of the workflow has assisted with optimization of logging programs utilizing technology with costly lost in hole charges and high rate of tool failures in offshore operations.\u0000 The optimization has reduced the requirement for logistics associated with logging and eliminated the need for radioactive sources and lithium batteries.\u0000 Highest precision in logging prediction has been achieved through an automated workflow for real time operations. In addition, the workflow can also be deployed with robotics technology to automate sample collection, leading to increased efficiencies.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129266798","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}
Fuad Atakishiyev, Alessandro Delfino, C. Cerrahoglu, Z. Hasanov, I. Yusifov, Anne Wallace, Alberto Mendoza
We introduce a novel Machine Learning (ML) approach for processing distributed fiber-optic sensing (DFOS) data that enables dynamic flow profile monitoring using a fiber-optic e-line cable deployed in a gas condensate well and compare it to a conventional approach. DFOS technology has the potential to provide more efficient and dynamic flow profiles compared to traditional methods, particularly in high rate gas wells where production logs (PL) are recorded at reduced rates to avoid tool lifting. Distributed acoustic and temperature sensing (DAS & DTS) data were acquired simultaneously while the well was producing ~70 MMSCF/D gas. Conventional PL data was also acquired under the same condition to validate the flow profiling results obtained from DFOS measurements. This paper describes a novel data processing approach where ML based models for pattern recognition were applied to obtain the signatures of different fluid types. Flow profiling is achieved by applying multiple data models to address three key questions for inflow profiling: (1) which zones are producing? (2) what is the phase? and (3) what is the flow rate? A blind test was set up to avoid results contamination. The processing and interpretation of DFOS data and PL data were carried out independently and results were compared only when the work on both datasets was completed. The comparison demonstrates a good match between two measurements for gas inflow profile with an average error of about 1% in relative gas rate allocation along the four producing perforated intervals. Flow profile in a single-phase gas producing well was accurately determined by DFOS data analysis and the liquid production rate was then re-calculated using condensate-gas ratio (CGR) to obtain liquid and gas production rates at standard surface condition. The well was connected to a test separator during the entire acquisition period, and accurate gas, condensate and water production rates were obtained in real-time at surface condition. The hybrid processing technique was applied for the first time among our well stock and resulted in accurate gas inflow profiling. To further validate the performance of the presented approach, the authors intend to repeat the test in other high rate gas producing wells, including wells with permanently installed fiber. Multi-disciplinary teamwork involved collaboration between operator and vendor and allowed for efficient operational execution. The result of the risk assessment ensured the selection of the best candidate well ensuring minimum sand production at the optimum production rate, optimization of stationary time for DFOS data acquisition and cable armor erosion model.
{"title":"Flow Diagnostics in High Rate Gas Condensate Well Using Distributed Fiber-Optic Sensing and its Validation with Conventional Production Log","authors":"Fuad Atakishiyev, Alessandro Delfino, C. Cerrahoglu, Z. Hasanov, I. Yusifov, Anne Wallace, Alberto Mendoza","doi":"10.2118/205435-ms","DOIUrl":"https://doi.org/10.2118/205435-ms","url":null,"abstract":"\u0000 We introduce a novel Machine Learning (ML) approach for processing distributed fiber-optic sensing (DFOS) data that enables dynamic flow profile monitoring using a fiber-optic e-line cable deployed in a gas condensate well and compare it to a conventional approach. DFOS technology has the potential to provide more efficient and dynamic flow profiles compared to traditional methods, particularly in high rate gas wells where production logs (PL) are recorded at reduced rates to avoid tool lifting.\u0000 Distributed acoustic and temperature sensing (DAS & DTS) data were acquired simultaneously while the well was producing ~70 MMSCF/D gas. Conventional PL data was also acquired under the same condition to validate the flow profiling results obtained from DFOS measurements. This paper describes a novel data processing approach where ML based models for pattern recognition were applied to obtain the signatures of different fluid types. Flow profiling is achieved by applying multiple data models to address three key questions for inflow profiling: (1) which zones are producing? (2) what is the phase? and (3) what is the flow rate?\u0000 A blind test was set up to avoid results contamination. The processing and interpretation of DFOS data and PL data were carried out independently and results were compared only when the work on both datasets was completed. The comparison demonstrates a good match between two measurements for gas inflow profile with an average error of about 1% in relative gas rate allocation along the four producing perforated intervals. Flow profile in a single-phase gas producing well was accurately determined by DFOS data analysis and the liquid production rate was then re-calculated using condensate-gas ratio (CGR) to obtain liquid and gas production rates at standard surface condition. The well was connected to a test separator during the entire acquisition period, and accurate gas, condensate and water production rates were obtained in real-time at surface condition.\u0000 The hybrid processing technique was applied for the first time among our well stock and resulted in accurate gas inflow profiling. To further validate the performance of the presented approach, the authors intend to repeat the test in other high rate gas producing wells, including wells with permanently installed fiber. Multi-disciplinary teamwork involved collaboration between operator and vendor and allowed for efficient operational execution. The result of the risk assessment ensured the selection of the best candidate well ensuring minimum sand production at the optimum production rate, optimization of stationary time for DFOS data acquisition and cable armor erosion model.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122062280","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. Kohli, O. Kelder, M. Volkov, Rita-Michel Greiss, Natalia Kudriavaya, A. Galyautdinov
When an oilfield is exploited by simply producing oil and gas from a number of wells, the reservoir pressure in many circumstances drops quicker than normal impacting the production rates (Koning, 1988) and well performance. To maintain the pressures in the oil producing formations, waterflooding enhancement method is implemented by the Operators. This is achieved by drilling injection wells or converting the oil producing wells into injectors. The injection wells are located at carefully selected points in the oilfield so that the water displaces as much oil as possible to the production wells before the water starts to break through. A significant saving in an oilfield development can be obtained by reducing the actual number of injecting wells and increasing each of the injector wells' capacity for injection. Balancing the injection and produced volumes often involves injecting at high pressures leading to the fracture of the reservoir rocks along a plane intersecting the wellbore. This happens when injection pressure overcomes the rock stress and its tensile strength, thereby creating an induced fracture network. With continuous injection, these fractures start propagating into the reservoir and may reach the reservoir caprock. Continuing to inject further in such a fracture system may breach the top seal integrity of the caprock leading to uncontrolled out of zone injection. The study of evaluation of downhole fracture monitoring is divided into two parts. In this paper a downhole verification approach to identify the fracture initiation point(s) is the focus. It describes the planning, execution and interpretation of the downhole data. This includes spectral acoustic monitoring and modelling of the temperature responses to quantify the injectivity profile. In paper (Kohli, Kelder, Volkov, Castelijns, & van Eijs, 2021), the direct business impact and regulatory requirements are discussed by further integration of acoustic monitoring and temperature modeling data with detailed results from downhole measurements of fracture dimensions by means of pressure fall off tests. Combined, both studies form the integrated approach that the Operator took to meet the regulatory requirements proving that the fracture network propagation remains within the reservoir and that the top seal integrity is maintained.
当一个油田仅仅通过多口井进行油气开采时，在许多情况下，油藏压力下降的速度比正常情况下要快，这会影响产量(Koning, 1988)和油井的性能。为了保持产油层的压力，作业者采用了水驱增强方法。这可以通过钻注水井或将产油井改造成注水井来实现。注水井位于油田中精心选择的位置，以便在水开始突破之前将尽可能多的油置换到生产井中。通过减少注入井的实际数量和增加每口注入井的注入能力，可以显著节省油田开发成本。为了平衡注入量和采出量，通常需要在高压下注入，导致储层岩石沿与井筒相交的平面破裂。当注入压力超过岩石应力及其抗拉强度时，就会发生这种情况，从而形成诱导裂缝网络。随着持续注入，这些裂缝开始向储层扩展，并可能到达储层盖层。在这样的裂缝系统中继续注入可能会破坏盖层顶部密封的完整性，导致无法控制的层外注入。井下裂缝监测评价的研究分为两个部分。本文的重点是确定裂缝起裂点的井下验证方法。它描述了井下数据的规划、执行和解释。这包括光谱声学监测和温度响应建模，以量化注入率剖面。在论文中(Kohli, Kelder, Volkov, Castelijns， & van Eijs, 2021)，通过进一步整合声学监测和温度建模数据，以及通过压力脱落测试获得的井下裂缝尺寸测量的详细结果，讨论了直接的业务影响和监管要求。综上所述，这两项研究形成了作业者采用的综合方法，以满足监管要求，证明裂缝网络仍然在储层内扩展，并且保持了顶部密封的完整性。
{"title":"Downhole Monitoring of Fractures in a Waterflood Development – Part 1","authors":"A. Kohli, O. Kelder, M. Volkov, Rita-Michel Greiss, Natalia Kudriavaya, A. Galyautdinov","doi":"10.2118/205461-ms","DOIUrl":"https://doi.org/10.2118/205461-ms","url":null,"abstract":"\u0000 When an oilfield is exploited by simply producing oil and gas from a number of wells, the reservoir pressure in many circumstances drops quicker than normal impacting the production rates (Koning, 1988) and well performance.\u0000 To maintain the pressures in the oil producing formations, waterflooding enhancement method is implemented by the Operators. This is achieved by drilling injection wells or converting the oil producing wells into injectors. The injection wells are located at carefully selected points in the oilfield so that the water displaces as much oil as possible to the production wells before the water starts to break through. A significant saving in an oilfield development can be obtained by reducing the actual number of injecting wells and increasing each of the injector wells' capacity for injection. Balancing the injection and produced volumes often involves injecting at high pressures leading to the fracture of the reservoir rocks along a plane intersecting the wellbore. This happens when injection pressure overcomes the rock stress and its tensile strength, thereby creating an induced fracture network. With continuous injection, these fractures start propagating into the reservoir and may reach the reservoir caprock. Continuing to inject further in such a fracture system may breach the top seal integrity of the caprock leading to uncontrolled out of zone injection.\u0000 The study of evaluation of downhole fracture monitoring is divided into two parts. In this paper a downhole verification approach to identify the fracture initiation point(s) is the focus. It describes the planning, execution and interpretation of the downhole data. This includes spectral acoustic monitoring and modelling of the temperature responses to quantify the injectivity profile.\u0000 In paper (Kohli, Kelder, Volkov, Castelijns, & van Eijs, 2021), the direct business impact and regulatory requirements are discussed by further integration of acoustic monitoring and temperature modeling data with detailed results from downhole measurements of fracture dimensions by means of pressure fall off tests. Combined, both studies form the integrated approach that the Operator took to meet the regulatory requirements proving that the fracture network propagation remains within the reservoir and that the top seal integrity is maintained.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114673606","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}
M. Schuberth, Håkon Sunde Bakka, C. Birnie, S. Dümmong, K. Haavik, Qin Li, J. Synnevåg, Yanis Saadallah, Lars Vinje, K. Constable
Fiber Optic (FO) sensing capabilities for downhole monitoring include, among other techniques, Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS). The appeal of DTS and DAS data is based on its high temporal and spatial sampling, allowing for very fine localization of processes in a wellbore. Furthermore, the broad frequency spectrum that especially DAS data is acquired with, enables observations, ranging from more continuous effects like oil flow, to more distinct effects like opening and closing of valves. Due to the high data volume of hundreds of Gb per well per hour, DAS data has traditionally been acquired acquisition-based, where data is recorded for a limited amount of time and processed at a later point in time. This limits the decision-making capability based on this data as reacting to events is only possible long after the event occurred. Equinor has addressed these decision-making shortcomings by building a real-time streaming solution for transferring, processing, and interpretation of its FO data at the Johan Sverdrup field in the North Sea. The streaming solution for FO data consists of offshore interrogators streaming raw DAS and DTS data via a dedicated bandwidth to an onshore processing cluster. There, DAS data is transformed into FO feature data, e.g., Frequency Band Energies, which are heavily decimated versions of the raw data; allowing insight extraction, while significantly reducing data volumes. DTS and DAS FO feature data are then streamed to a custom-made, cloud-based visualization and integration platform. This cloud-based platform allows efficient inspection of large data sets, control and evaluation of applications based on these data, and sharing of FO data within the Johan Sverdrup asset. During the last year, this FO data streaming pipeline has processed several tens of PB of FO data, monitoring a range of well operations and processes. Qualitatively, the benefits and potential of the real-time data acquisitions have been illustrated by providing a greater understanding of current well conditions and processes. Alongside the FO data pipeline, multiple prototype applications have been developed for automated monitoring of Gas Lift Valves, Safety Valve operations, Gas Lift rate estimation, and monitoring production start-up, all providing insights in real-time. For certain use cases, such as monitoring production start-up, the FO data provides a previously non-existent monitoring solution. In this paper, we will discuss in detail the FO data pipeline architecture from-platform-to-cloud, illustrate several data examples, and discuss the way-forward for "real-time" FO data analytics.
{"title":"A Real-Time Fiber Optical System for Wellbore Monitoring: A Johan Sverdrup Case Study","authors":"M. Schuberth, Håkon Sunde Bakka, C. Birnie, S. Dümmong, K. Haavik, Qin Li, J. Synnevåg, Yanis Saadallah, Lars Vinje, K. Constable","doi":"10.2118/205405-ms","DOIUrl":"https://doi.org/10.2118/205405-ms","url":null,"abstract":"\u0000 Fiber Optic (FO) sensing capabilities for downhole monitoring include, among other techniques, Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS). The appeal of DTS and DAS data is based on its high temporal and spatial sampling, allowing for very fine localization of processes in a wellbore. Furthermore, the broad frequency spectrum that especially DAS data is acquired with, enables observations, ranging from more continuous effects like oil flow, to more distinct effects like opening and closing of valves.\u0000 Due to the high data volume of hundreds of Gb per well per hour, DAS data has traditionally been acquired acquisition-based, where data is recorded for a limited amount of time and processed at a later point in time. This limits the decision-making capability based on this data as reacting to events is only possible long after the event occurred. Equinor has addressed these decision-making shortcomings by building a real-time streaming solution for transferring, processing, and interpretation of its FO data at the Johan Sverdrup field in the North Sea.\u0000 The streaming solution for FO data consists of offshore interrogators streaming raw DAS and DTS data via a dedicated bandwidth to an onshore processing cluster. There, DAS data is transformed into FO feature data, e.g., Frequency Band Energies, which are heavily decimated versions of the raw data; allowing insight extraction, while significantly reducing data volumes. DTS and DAS FO feature data are then streamed to a custom-made, cloud-based visualization and integration platform. This cloud-based platform allows efficient inspection of large data sets, control and evaluation of applications based on these data, and sharing of FO data within the Johan Sverdrup asset.\u0000 During the last year, this FO data streaming pipeline has processed several tens of PB of FO data, monitoring a range of well operations and processes. Qualitatively, the benefits and potential of the real-time data acquisitions have been illustrated by providing a greater understanding of current well conditions and processes. Alongside the FO data pipeline, multiple prototype applications have been developed for automated monitoring of Gas Lift Valves, Safety Valve operations, Gas Lift rate estimation, and monitoring production start-up, all providing insights in real-time. For certain use cases, such as monitoring production start-up, the FO data provides a previously non-existent monitoring solution.\u0000 In this paper, we will discuss in detail the FO data pipeline architecture from-platform-to-cloud, illustrate several data examples, and discuss the way-forward for \"real-time\" FO data analytics.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134099446","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}
This paper will present a software that was developed to diagnose well test data. The software monitors the data, and through a series of algorithms alarms the user in case of discrepancies. This allows the user to investigate possible source of errors and correct them in real time. Several datasets from previous operations were analyzed and the basic physics governing how a certain datum depends on others were laid out. All the well test data traditionally acquired were put on a matrix, showing the dependencies between each datum and other physical properties that are available - either measured or modelled. Acceptable fluctuations in acquired data were also identified for use as tolerance limits. The software scans through the data as it is acquired and raises an alarm when the identified dependencies are broken. The software also identified which parameter is most likely causing the error. The software was built based on previous well test data and reports. Subsequently, two field trials were conducted to fine tune the algorithms and allowable data fluctuations. The process of validating the software consisted of: (1) Identifying flagged errors that should have not been flagged (dependencies set too tight); (2) identifying errors that should have been flagged and were not (dependencies set too loose); (3) improving the user interface for ease of use. The results were positive, with several improvements in the error recognition and several discrepancies flagged that would not have been caught by the naked eye. The user interface was also improved, allowing the user to clear error messages and provide input to improve the algorithm. The field trial also demonstrated that the methodology is scalable to other data acquisition plans and to more advanced analytics. The algorithms are simple, allowing the software to be implemented in all operations. More advanced algorithms are likely to depend on job specific data and parameters. Traditional data acquisition systems used during well test only present the data. Alarms trigger the user's attention only when certain defined operability limits are about to be reached. Being able to confirm that the data is cohesive during the well test prevents a loss of confidence in the results and painful post processing exercises. Moreover, given the algorithms used are based on simple physics, it is easy to deploy the software in any operation.
{"title":"Data Analytics Software for Automatic Detection of Anomalies in Well Testing","authors":"Stefano Capponi, Chiazor Nwachukwu","doi":"10.2118/205456-ms","DOIUrl":"https://doi.org/10.2118/205456-ms","url":null,"abstract":"\u0000 This paper will present a software that was developed to diagnose well test data. The software monitors the data, and through a series of algorithms alarms the user in case of discrepancies. This allows the user to investigate possible source of errors and correct them in real time.\u0000 Several datasets from previous operations were analyzed and the basic physics governing how a certain datum depends on others were laid out. All the well test data traditionally acquired were put on a matrix, showing the dependencies between each datum and other physical properties that are available - either measured or modelled. Acceptable fluctuations in acquired data were also identified for use as tolerance limits. The software scans through the data as it is acquired and raises an alarm when the identified dependencies are broken. The software also identified which parameter is most likely causing the error.\u0000 The software was built based on previous well test data and reports. Subsequently, two field trials were conducted to fine tune the algorithms and allowable data fluctuations. The process of validating the software consisted of: (1) Identifying flagged errors that should have not been flagged (dependencies set too tight); (2) identifying errors that should have been flagged and were not (dependencies set too loose); (3) improving the user interface for ease of use. The results were positive, with several improvements in the error recognition and several discrepancies flagged that would not have been caught by the naked eye. The user interface was also improved, allowing the user to clear error messages and provide input to improve the algorithm. The field trial also demonstrated that the methodology is scalable to other data acquisition plans and to more advanced analytics. The algorithms are simple, allowing the software to be implemented in all operations. More advanced algorithms are likely to depend on job specific data and parameters.\u0000 Traditional data acquisition systems used during well test only present the data. Alarms trigger the user's attention only when certain defined operability limits are about to be reached. Being able to confirm that the data is cohesive during the well test prevents a loss of confidence in the results and painful post processing exercises. Moreover, given the algorithms used are based on simple physics, it is easy to deploy the software in any operation.","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132711124","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}
One emerging application in geothermal energy is that of closed-loop systems, where two laterals are intersected so that a working fluid can be pumped down one wellhead and up another. These solutions are attractive because they do not rely on the natural permeability of a formation or a reservoir of heated water already in place, they simply require a high enough downhole temperature. While a great deal of discussion exists on wellbore intersection, most applications are by their nature heavily constrained by tight geologic requirements (e.g. coal-bed methane) or have one wellbore trajectory rigidly defined (e.g. relief well drilling). These intersection operations require extensive use of specialized ranging technologies and control drilling at the intersection point which can be time-consuming. Closed-loop geothermal presents a unique opportunity, with relatively few constraints to satisfy (e.g. target depth, lateral length). This study uses this freedom in trajectory design and quantifies the extent that various wellbore positioning techniques can increase the probability of intersection while minimizing the need for ranging workflows. A baseline scenario is described, with wells originating from differing pad locations, drilling with standard practices and active magnetic ranging. Using Monte Carlo techniques, the probability of successful intercept is evaluated for alternate trajectory combinations and compared to the baseline. These include well pairs originating from the same pad and pairs from differing pad locations. Major factors contributing to relative survey errors are identified and the impact of uncertainty reducing techniques are explored for each trajectory type. Techniques include survey corrections, variation of the trajectory profiles, incidence angle at intersection, and the use of alternative solutions to control relative vertical uncertainty. For each scenario, the probability of intercept was evaluated for cases without using ranging tools and for both passive and active ranging technologies. A cost-benefit comparison is conducted, and an optimal combination of factors is identified. For the baseline scenario, low probabilities of collision imply that extensive use of ranging is required for a successful operation. Positional uncertainty reduction techniques and multiple target intervals can greatly increase the collision probability and reduce the need for ranging. Of importance to increasing the probability of successful interception are techniques that maximize the uncertainty reduction along a single axis (e.g. the vertical plane). This enables a "sweep" across the other plane to achieve intersection. Value provided by additional uncertainty reduction techniques depends on the assumed costs of drilling additional footage, performing ranging operations, and rig spread rate. The application of sophisticated wellbore positioning techniques at scale to the closed-loop geothermal problem has not been previously explor
{"title":"Optimizing Wellbore Trajectories for Closed Loop Geothermal Operations","authors":"A. McGregor, Marc E. Willerth, Nishant Agarwal","doi":"10.2118/205450-ms","DOIUrl":"https://doi.org/10.2118/205450-ms","url":null,"abstract":"\u0000 One emerging application in geothermal energy is that of closed-loop systems, where two laterals are intersected so that a working fluid can be pumped down one wellhead and up another. These solutions are attractive because they do not rely on the natural permeability of a formation or a reservoir of heated water already in place, they simply require a high enough downhole temperature. While a great deal of discussion exists on wellbore intersection, most applications are by their nature heavily constrained by tight geologic requirements (e.g. coal-bed methane) or have one wellbore trajectory rigidly defined (e.g. relief well drilling). These intersection operations require extensive use of specialized ranging technologies and control drilling at the intersection point which can be time-consuming. Closed-loop geothermal presents a unique opportunity, with relatively few constraints to satisfy (e.g. target depth, lateral length). This study uses this freedom in trajectory design and quantifies the extent that various wellbore positioning techniques can increase the probability of intersection while minimizing the need for ranging workflows.\u0000 A baseline scenario is described, with wells originating from differing pad locations, drilling with standard practices and active magnetic ranging. Using Monte Carlo techniques, the probability of successful intercept is evaluated for alternate trajectory combinations and compared to the baseline. These include well pairs originating from the same pad and pairs from differing pad locations. Major factors contributing to relative survey errors are identified and the impact of uncertainty reducing techniques are explored for each trajectory type. Techniques include survey corrections, variation of the trajectory profiles, incidence angle at intersection, and the use of alternative solutions to control relative vertical uncertainty. For each scenario, the probability of intercept was evaluated for cases without using ranging tools and for both passive and active ranging technologies. A cost-benefit comparison is conducted, and an optimal combination of factors is identified.\u0000 For the baseline scenario, low probabilities of collision imply that extensive use of ranging is required for a successful operation. Positional uncertainty reduction techniques and multiple target intervals can greatly increase the collision probability and reduce the need for ranging. Of importance to increasing the probability of successful interception are techniques that maximize the uncertainty reduction along a single axis (e.g. the vertical plane). This enables a \"sweep\" across the other plane to achieve intersection. Value provided by additional uncertainty reduction techniques depends on the assumed costs of drilling additional footage, performing ranging operations, and rig spread rate.\u0000 The application of sophisticated wellbore positioning techniques at scale to the closed-loop geothermal problem has not been previously explor","PeriodicalId":421935,"journal":{"name":"Day 1 Tue, September 07, 2021","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125343864","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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