Steven Knabe - Academia.edu (original) (raw)

Papers by Steven Knabe

SPE Annual Technical Conference and Exhibition, 2016

SPE Annual Technical Conference and Exhibition, 2016

Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren ... more Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren von Fordergeschichtenzuordnung durch Berechnen einer Nichtubereinstimmung fur mehrere Realisationen eines Geomodells, das eine Lagerstatte darstellt; b) Auswahlen eines Produktionsbohrlochs aus einer Gruppe von Produktionsbohrlochern in der Lagerstatte; c) Erzeugen von einer oder mehreren Musterrealisationen fur das Geomodell durch probeweises Entnehmen von einer oder mehreren physikalischen Gitterzelleneigenschaften an einer oder mehreren Stromlinientrajektorien von einer oder mehreren der mehreren Realisationen des Geomodells, die ein vorgegebenes Bewertungskriterium erfullen, wobei die eine oder die mehreren Stromlinientrajektorien das ausgewahlte Produktionsbohrloch mit wenigstens einem von einem Flutbohrloch, einem Aquifer und einer Gaskappe verbinden; d) Aktualisieren einer oder mehrerer der mehreren Realisationen fur das ausgewahlte Produktionsbohrloch mithilfe der einen oder der ...

Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren ... more Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren von Fordergeschichtenzuordnung durch Berechnen einer Nichtubereinstimmung fur mehrere Realisationen eines Geomodells, das eine Lagerstatte darstellt; b) Auswahlen eines Produktionsbohrlochs aus einer Gruppe von Produktionsbohrlochern in der Lagerstatte; c) Erzeugen von einer oder mehreren Musterrealisationen fur das Geomodell durch probeweises Entnehmen von einer oder mehreren physikalischen Gitterzelleneigenschaften an einer oder mehreren Stromlinientrajektorien von einer oder mehreren der mehreren Realisationen des Geomodells, die ein vorgegebenes Bewertungskriterium erfullen, wobei die eine oder die mehreren Stromlinientrajektorien das ausgewahlte Produktionsbohrloch mit wenigstens einem von einem Flutbohrloch, einem Aquifer und einer Gaskappe verbinden; d) Aktualisieren einer oder mehrerer der mehreren Realisationen fur das ausgewahlte Produktionsbohrloch mithilfe der einen oder der ...

Proc. Indon Petrol. Assoc., 32nd Ann. Conv.

Many sensors and devices in the field are available for constant monitoring at the reservoir mana... more Many sensors and devices in the field are available for constant monitoring at the reservoir manager's desk, but the industry finds itself faced with the challenge of transforming all this raw streaming data into useful information. At short periods, data are used to identify anomalies and provide alarms in the classical Field Automation sense. In the medium to long term, data should also be used to identify well and reservoir signatures for formation evaluation, reservoir management and planning through the use of flow simulation and automated history matching.

Proc. Indon Petrol. Assoc., 32nd Ann. Conv.

Many sensors and devices in the field are available for constant monitoring at the reservoir mana... more Many sensors and devices in the field are available for constant monitoring at the reservoir manager's desk, but the industry finds itself faced with the challenge of transforming all this raw streaming data into useful information. At short periods, data are used to identify anomalies and provide alarms in the classical Field Automation sense. In the medium to long term, data should also be used to identify well and reservoir signatures for formation evaluation, reservoir management and planning through the use of flow simulation and automated history matching.

SPE Intelligent Energy Conference & Exhibition, 2014

Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D... more Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D 5 and whose discriminant is bounded by X. By a conjecture of Malle, we expect that N (5, D 5 , X) ∼ C • X 1 2 for some constant C. The best known upper bound is N (5, D 5 , X) X 3 4 +ε , and we show this could be improved by counting points on a certain variety defined by a norm equation; computer calculations give strong evidence that this number is X 2 3. Finally, we show how such norm equations can be helpful by reinterpreting an earlier proof of Wong on upper bounds for A 4 quartic fields in terms of a similar norm equation.

SPE Intelligent Energy Conference & Exhibition, 2014

Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D... more Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D 5 and whose discriminant is bounded by X. By a conjecture of Malle, we expect that N (5, D 5 , X) ∼ C • X 1 2 for some constant C. The best known upper bound is N (5, D 5 , X) X 3 4 +ε , and we show this could be improved by counting points on a certain variety defined by a norm equation; computer calculations give strong evidence that this number is X 2 3. Finally, we show how such norm equations can be helpful by reinterpreting an earlier proof of Wong on upper bounds for A 4 quartic fields in terms of a similar norm equation.

International Petroleum Technology Conference, 2014

This paper outlines the visualization and collaboration attributes of an automated workflow that ... more This paper outlines the visualization and collaboration attributes of an automated workflow that integrates the computer-assisted history matching (AHM), quantification of inherent model uncertainty, and optimization on production-forecast decisions. The workflow belongs to the group of smart flows for integrated asset management installed at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. The workflow is facilitated through four interactive user interfaces:Dashboard: displays history-match indices for water cut and visualizes maps of permeability, porosity, water and oil saturation, reservoir quality index, and reservoir pressure.Field and Well History Matching: displays well-level history matching and forecasting results filtered by water cut, bottomhole pressure (BHP), and liquid rate and visualizes the distributions of corresponding errors per simulated scenario.Dynamic Ranking: categorizes and ranks trends of forecasted oil recovery for history-matched models using multidimensional scaling and clustering techniques and visualizes identified P10, P50, and P90 models.Property Comparison: displays permeability maps for prior and history-matched models to identify the regions of improvement in terms of reservoir heterogeneity. Additionally, streamline trajectories colored by the time-of-flight provide excellent visualization of reservoir connectivity. The workflow was applied in the pilot area of a major Middle East carbonate reservoir in North Kuwait and performs complex history matching and production forecasting. The simulation model combines 49 wells in 5 waterflood patterns to match 50 years of oil production, 12 years of water injection, and 8 years of forecasting. The differentiator of this workflow is that it is unique in direct interfacing between the geomodeling application and reservoir simulator and in updating of high-resolution models with no upscaling. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with production, completion, and geological information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture. Introduction With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive future KOC operations to the next level of excellence, the operator's senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as "smart flows," to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at the KwIDF collaboration center in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013a) and references therein.

International Petroleum Technology Conference, 2014

History matching, being an ill-posed optimization problem, attempts to render multiple realizatio... more History matching, being an ill-posed optimization problem, attempts to render multiple realizations of reservoir models that satisfy a given objective function with applicable constraints. A variety of assisted history-matching (AHM) techniques is currently being developed and used with the main objective to generate statistically diverse ensembles of history-matched models to capture the uncertainty in the distribution of reservoir parameters. This paper targets the outstanding questions of how to a) rigorously quantify the uncertainty in the distribution of the most prominent reservoir parameters that govern the reservoir connectivity and b) rank the history-matched models and identify the model candidates for production forecasting without compromising the notion of uncertainty. A workflow has been developed that integrates the modules for AHM and dynamic model ranking (DMR) based on forecasted oil recovery factors (ORFs). A pattern recognition methodology based on a kernel -means clustering algorithm is used to identify key reservoir models. The reduced set of models is used to minimize the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM and DMR workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. Introduction Reservoir characterization is one of the most important and comprehensive tasks in the process of field development planning (FDP). The key component in FDP is developing a history-matched reservoir model that correctly represents the physics of the actual reservoir, reconciles with logging and historic production data, and accounts for reservoir uncertainties. Because of the nature of the inverse problem, history matching delivers no unique solution, and multiple history-matched models can satisfy the objective function within given constraints. Recently, a variety of algorithms (Schulze-Riegert and Ghedan 2007; Oliver and Chen 2011; Rwenchungura et al. 2011; Maučec et al. 2013a and references therein) have been considered that enable AHM and uncertainty quantification; however, there are still unanswered questions:How to efficiently utilize and extract the knowledge from the many history-matched models for FDP.Which model should be chosen for the production forecasting?If it is possible to identify and optimize, on a few representative models for which FDP or any forecast-based analysis should be performed, while still comprehensively capturing the uncertainty. The present work focuses particularly on addressing these questions, with a field case study applied to the Sabriyah-Mauddud (SaMa) reservoir operated by the Kuwait Oil Company (KOC). As part of a comprehensive strategy to enhance the overall productivity of reservoirs through the application of intelligent digital oilfield (iDOF) concepts, KOC has already initiated an assessment of their major SaMa reservoir for conversion to an integrated iDOF master platform (Al-Abbasi et al. 2013; Al-Jasmi et al. 2013 and references therein). KOC is also aiming to increase efficiency by automating work processes and shortening observation-to-action cycle time. With these goals in mind, several next generation smart workflows have been delivered to KOC. These workflows combine subsurface waterflooding optimization (SWFO) (Khan et al. 2013), integrated production optimization (IPO), and simulation model update and ranking (SMUR) (Maučec et al. 2013a).

SPE Middle East Intelligent Energy Conference and Exhibition, 2013

SPE Middle East Intelligent Energy Conference and Exhibition, 2013

International Petroleum Technology Conference, 2014

This paper introduces the bases for the design of next-generation automated workflows to implemen... more This paper introduces the bases for the design of next-generation automated workflows to implement advanced assisted history-matching (AHM) techniques. The paper presents procedures for geostatistical modeling, high-end dynamic flow simulation modeling, and the use of streamline tracing and visualization to generate a basic (fundamental) model for AHM. The accuracy of the base model is essential because this model is the starting point of the AHM process; therefore, the quality of the AHM process is dependent on the base model. The geomodel benefits from a combination of multiple lithotype proportion mapping (LPM) and plurigaussian simulation (PGS), which successfully represents complex, carbonate depositional settings with eight lithofacies and high-permeability channels. By honoring geostatistical variograms and core-log constraints, a reservoir model is generated with 1.4 million cells. The LPM indicated that 108 layers are sufficient to describe the vertical resolution of lithofacies in the reservoir. A three-dimensional (3D), three-phase, black-oil single-porosity numerical simulation model was developed. The dynamic model has three-phase relative permeability normalization that computes the effects of parameterizing rock type and permeability distribution in the static model. The model is complex, as it has 16 equilibrium regions and two pressure volume temperature (PVT) regions. The simulation model includes 49 wells in 5 waterflood patterns to match 50 years of production, 12 years of injection, and 8 years of forecasting. The model was optimized for minimum simulation time. The base case was used for a) closed-loop, multilevel probabilistic history matching with parameterization of geostatistical and reservoir-dynamic properties and b) dynamic model ranking (DMR) and uncertainty quantification based on predicted oil recovery factor (ORF). This workflow was implemented at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. It generates faster and more accurate history matching updates, produces a high-resolution reservoir model with no upscaling, and calculates waterflood indicators, including voidage replacement, water injector efficiency, producer well allocations, sweep efficiencies, and recovery factors. Introduction With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive the future KOC operations to the next level of excellence, the operator's senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as "smart flows," to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at KwIDF in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013) and references therein.

EAGE Annual Conference & Exhibition incorporating SPE Europec, 2013

Intelligent digital oilfield operations collect real-time data from an operating asset and transf... more Intelligent digital oilfield operations collect real-time data from an operating asset and transform that raw data into information through intelligent, automated work processes, which assist engineers with key well operations and monitoring, improving their productivity and decision-making. A major oil and gas operator in the Middle East is developing a set of intelligent workflows for key activities and processes for its production operations, with the ultimate goal of improved asset performance. Real-time surveillance and monitoring of production operation processes have proven to be operationally and economically important for managing complex, high-cost reservoirs. However, predicting short-term production and production interruptions—for example, related to pump settings—has posed a tremendous challenge. While operators routinely forecast production for the next 60 to 90 days, sophisticated tools such as full-field numerical simulation models are of limited use in predicting s...

A system of Water-Alternating-Gas (WAG) injection for enhanced oil recovery (Enhanced Oil Recover... more A system of Water-Alternating-Gas (WAG) injection for enhanced oil recovery (Enhanced Oil Recovery, EOR) comprises a mechanical drill hole, which is configured to permit selective multi-point injection of water and gas. The system also includes an optimization engine, which is designed for the calculation of reservoir fluid dynamics and for the selective injection of water and gas through the mechanical wellbore in accordance with the deposit of flow dynamics.

Offshore Technology Conference Brasil

SPE Kuwait Oil and Gas Show and Conference, 2013

Traditional reconciliation of geomodels with production data is one of the most laborious tasks i... more Traditional reconciliation of geomodels with production data is one of the most laborious tasks in reservoir engineering. The uncertainty associated with the great majority of model variables only adds to the overall complexity. This paper describes the conceptualization, implementation, and visualization characteristics of the multilevel assisted history matching (AHM) technique that captures inherent model uncertainty and allows for better quantification of production forecasts. The workflow is applied to history matching of the pilot area in a major, structurally complex Middle East (ME) carbonate reservoir. The simulation model combines 49 wells in five waterflood patterns to match 50 years of oil production and 12 years of water injection and to predict eight years of production. Initially, the reservoir model was calibrated to match oil production by modifying permeability and/or porosity at well locations and by fine-tuning rock-type properties and water saturation. The second level history match implemented two-stage Markov chain Monte Carlo (McMC) stochastic optimization to minimize the misfit in water cut on a well-by-well basis. The inversion process is dramatically accelerated by the efficient parameterization of permeability, constraining the proxy model using streamline-based sensitivities and using parallel and cluster computing. The optimal number of representative history-matched models was identified to capture the uncertainty in reservoir spatial connectivity using rigorous optimization and dynamic model ranking based on forecasted oil recovery factors (ORFs). The reduced set of models minimized the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture.

SPE Annual Technical Conference and Exhibition, 2016

SPE Annual Technical Conference and Exhibition, 2016

Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren ... more Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren von Fordergeschichtenzuordnung durch Berechnen einer Nichtubereinstimmung fur mehrere Realisationen eines Geomodells, das eine Lagerstatte darstellt; b) Auswahlen eines Produktionsbohrlochs aus einer Gruppe von Produktionsbohrlochern in der Lagerstatte; c) Erzeugen von einer oder mehreren Musterrealisationen fur das Geomodell durch probeweises Entnehmen von einer oder mehreren physikalischen Gitterzelleneigenschaften an einer oder mehreren Stromlinientrajektorien von einer oder mehreren der mehreren Realisationen des Geomodells, die ein vorgegebenes Bewertungskriterium erfullen, wobei die eine oder die mehreren Stromlinientrajektorien das ausgewahlte Produktionsbohrloch mit wenigstens einem von einem Flutbohrloch, einem Aquifer und einer Gaskappe verbinden; d) Aktualisieren einer oder mehrerer der mehreren Realisationen fur das ausgewahlte Produktionsbohrloch mithilfe der einen oder der ...

Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren ... more Ein Verfahren zur hybriden unterstutzten Fordergeschichtenzuordnung, beinhaltend: a) Durchfuhren von Fordergeschichtenzuordnung durch Berechnen einer Nichtubereinstimmung fur mehrere Realisationen eines Geomodells, das eine Lagerstatte darstellt; b) Auswahlen eines Produktionsbohrlochs aus einer Gruppe von Produktionsbohrlochern in der Lagerstatte; c) Erzeugen von einer oder mehreren Musterrealisationen fur das Geomodell durch probeweises Entnehmen von einer oder mehreren physikalischen Gitterzelleneigenschaften an einer oder mehreren Stromlinientrajektorien von einer oder mehreren der mehreren Realisationen des Geomodells, die ein vorgegebenes Bewertungskriterium erfullen, wobei die eine oder die mehreren Stromlinientrajektorien das ausgewahlte Produktionsbohrloch mit wenigstens einem von einem Flutbohrloch, einem Aquifer und einer Gaskappe verbinden; d) Aktualisieren einer oder mehrerer der mehreren Realisationen fur das ausgewahlte Produktionsbohrloch mithilfe der einen oder der ...

Proc. Indon Petrol. Assoc., 32nd Ann. Conv.

Many sensors and devices in the field are available for constant monitoring at the reservoir mana... more Many sensors and devices in the field are available for constant monitoring at the reservoir manager's desk, but the industry finds itself faced with the challenge of transforming all this raw streaming data into useful information. At short periods, data are used to identify anomalies and provide alarms in the classical Field Automation sense. In the medium to long term, data should also be used to identify well and reservoir signatures for formation evaluation, reservoir management and planning through the use of flow simulation and automated history matching.

Proc. Indon Petrol. Assoc., 32nd Ann. Conv.

Many sensors and devices in the field are available for constant monitoring at the reservoir mana... more Many sensors and devices in the field are available for constant monitoring at the reservoir manager's desk, but the industry finds itself faced with the challenge of transforming all this raw streaming data into useful information. At short periods, data are used to identify anomalies and provide alarms in the classical Field Automation sense. In the medium to long term, data should also be used to identify well and reservoir signatures for formation evaluation, reservoir management and planning through the use of flow simulation and automated history matching.

SPE Intelligent Energy Conference & Exhibition, 2014

Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D... more Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D 5 and whose discriminant is bounded by X. By a conjecture of Malle, we expect that N (5, D 5 , X) ∼ C • X 1 2 for some constant C. The best known upper bound is N (5, D 5 , X) X 3 4 +ε , and we show this could be improved by counting points on a certain variety defined by a norm equation; computer calculations give strong evidence that this number is X 2 3. Finally, we show how such norm equations can be helpful by reinterpreting an earlier proof of Wong on upper bounds for A 4 quartic fields in terms of a similar norm equation.

SPE Intelligent Energy Conference & Exhibition, 2014

Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D... more Let N (5, D 5 , X) be the number of quintic number fields whose Galois closure has Galois group D 5 and whose discriminant is bounded by X. By a conjecture of Malle, we expect that N (5, D 5 , X) ∼ C • X 1 2 for some constant C. The best known upper bound is N (5, D 5 , X) X 3 4 +ε , and we show this could be improved by counting points on a certain variety defined by a norm equation; computer calculations give strong evidence that this number is X 2 3. Finally, we show how such norm equations can be helpful by reinterpreting an earlier proof of Wong on upper bounds for A 4 quartic fields in terms of a similar norm equation.

International Petroleum Technology Conference, 2014

This paper outlines the visualization and collaboration attributes of an automated workflow that ... more This paper outlines the visualization and collaboration attributes of an automated workflow that integrates the computer-assisted history matching (AHM), quantification of inherent model uncertainty, and optimization on production-forecast decisions. The workflow belongs to the group of smart flows for integrated asset management installed at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. The workflow is facilitated through four interactive user interfaces:Dashboard: displays history-match indices for water cut and visualizes maps of permeability, porosity, water and oil saturation, reservoir quality index, and reservoir pressure.Field and Well History Matching: displays well-level history matching and forecasting results filtered by water cut, bottomhole pressure (BHP), and liquid rate and visualizes the distributions of corresponding errors per simulated scenario.Dynamic Ranking: categorizes and ranks trends of forecasted oil recovery for history-matched models using multidimensional scaling and clustering techniques and visualizes identified P10, P50, and P90 models.Property Comparison: displays permeability maps for prior and history-matched models to identify the regions of improvement in terms of reservoir heterogeneity. Additionally, streamline trajectories colored by the time-of-flight provide excellent visualization of reservoir connectivity. The workflow was applied in the pilot area of a major Middle East carbonate reservoir in North Kuwait and performs complex history matching and production forecasting. The simulation model combines 49 wells in 5 waterflood patterns to match 50 years of oil production, 12 years of water injection, and 8 years of forecasting. The differentiator of this workflow is that it is unique in direct interfacing between the geomodeling application and reservoir simulator and in updating of high-resolution models with no upscaling. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with production, completion, and geological information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture. Introduction With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive future KOC operations to the next level of excellence, the operator's senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as "smart flows," to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at the KwIDF collaboration center in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013a) and references therein.

International Petroleum Technology Conference, 2014

History matching, being an ill-posed optimization problem, attempts to render multiple realizatio... more History matching, being an ill-posed optimization problem, attempts to render multiple realizations of reservoir models that satisfy a given objective function with applicable constraints. A variety of assisted history-matching (AHM) techniques is currently being developed and used with the main objective to generate statistically diverse ensembles of history-matched models to capture the uncertainty in the distribution of reservoir parameters. This paper targets the outstanding questions of how to a) rigorously quantify the uncertainty in the distribution of the most prominent reservoir parameters that govern the reservoir connectivity and b) rank the history-matched models and identify the model candidates for production forecasting without compromising the notion of uncertainty. A workflow has been developed that integrates the modules for AHM and dynamic model ranking (DMR) based on forecasted oil recovery factors (ORFs). A pattern recognition methodology based on a kernel -means clustering algorithm is used to identify key reservoir models. The reduced set of models is used to minimize the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM and DMR workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. Introduction Reservoir characterization is one of the most important and comprehensive tasks in the process of field development planning (FDP). The key component in FDP is developing a history-matched reservoir model that correctly represents the physics of the actual reservoir, reconciles with logging and historic production data, and accounts for reservoir uncertainties. Because of the nature of the inverse problem, history matching delivers no unique solution, and multiple history-matched models can satisfy the objective function within given constraints. Recently, a variety of algorithms (Schulze-Riegert and Ghedan 2007; Oliver and Chen 2011; Rwenchungura et al. 2011; Maučec et al. 2013a and references therein) have been considered that enable AHM and uncertainty quantification; however, there are still unanswered questions:How to efficiently utilize and extract the knowledge from the many history-matched models for FDP.Which model should be chosen for the production forecasting?If it is possible to identify and optimize, on a few representative models for which FDP or any forecast-based analysis should be performed, while still comprehensively capturing the uncertainty. The present work focuses particularly on addressing these questions, with a field case study applied to the Sabriyah-Mauddud (SaMa) reservoir operated by the Kuwait Oil Company (KOC). As part of a comprehensive strategy to enhance the overall productivity of reservoirs through the application of intelligent digital oilfield (iDOF) concepts, KOC has already initiated an assessment of their major SaMa reservoir for conversion to an integrated iDOF master platform (Al-Abbasi et al. 2013; Al-Jasmi et al. 2013 and references therein). KOC is also aiming to increase efficiency by automating work processes and shortening observation-to-action cycle time. With these goals in mind, several next generation smart workflows have been delivered to KOC. These workflows combine subsurface waterflooding optimization (SWFO) (Khan et al. 2013), integrated production optimization (IPO), and simulation model update and ranking (SMUR) (Maučec et al. 2013a).

SPE Middle East Intelligent Energy Conference and Exhibition, 2013

SPE Middle East Intelligent Energy Conference and Exhibition, 2013

International Petroleum Technology Conference, 2014

This paper introduces the bases for the design of next-generation automated workflows to implemen... more This paper introduces the bases for the design of next-generation automated workflows to implement advanced assisted history-matching (AHM) techniques. The paper presents procedures for geostatistical modeling, high-end dynamic flow simulation modeling, and the use of streamline tracing and visualization to generate a basic (fundamental) model for AHM. The accuracy of the base model is essential because this model is the starting point of the AHM process; therefore, the quality of the AHM process is dependent on the base model. The geomodel benefits from a combination of multiple lithotype proportion mapping (LPM) and plurigaussian simulation (PGS), which successfully represents complex, carbonate depositional settings with eight lithofacies and high-permeability channels. By honoring geostatistical variograms and core-log constraints, a reservoir model is generated with 1.4 million cells. The LPM indicated that 108 layers are sufficient to describe the vertical resolution of lithofacies in the reservoir. A three-dimensional (3D), three-phase, black-oil single-porosity numerical simulation model was developed. The dynamic model has three-phase relative permeability normalization that computes the effects of parameterizing rock type and permeability distribution in the static model. The model is complex, as it has 16 equilibrium regions and two pressure volume temperature (PVT) regions. The simulation model includes 49 wells in 5 waterflood patterns to match 50 years of production, 12 years of injection, and 8 years of forecasting. The model was optimized for minimum simulation time. The base case was used for a) closed-loop, multilevel probabilistic history matching with parameterization of geostatistical and reservoir-dynamic properties and b) dynamic model ranking (DMR) and uncertainty quantification based on predicted oil recovery factor (ORF). This workflow was implemented at the North Kuwait Integrated Digital Field (KwIDF) collaboration center. It generates faster and more accurate history matching updates, produces a high-resolution reservoir model with no upscaling, and calculates waterflood indicators, including voidage replacement, water injector efficiency, producer well allocations, sweep efficiencies, and recovery factors. Introduction With the vision to transform the Kuwait Oil Company (KOC) through the application of integrated digital oilfield (iDOF) concepts and drive the future KOC operations to the next level of excellence, the operator's senior management endorsed the development of a family of advanced integrated asset management (IAM) workflows, referred to as "smart flows," to optimize and integrate the subsurface models of the major Sabriyah-Mauddud (SaMa) reservoir with well models and network surface systems in various time horizons. The objective is to increase the effectiveness through automating work processes and shortening observation-to-action cycle time. The group of nine first-generation production engineering workflows focuses on production and operational activities and was launched at KwIDF in 2012. The workflows are introduced in Al-Abbasi et al. (2013) and described in greater detail in Al-Jasmi et al. (2013) and references therein.

EAGE Annual Conference & Exhibition incorporating SPE Europec, 2013

Intelligent digital oilfield operations collect real-time data from an operating asset and transf... more Intelligent digital oilfield operations collect real-time data from an operating asset and transform that raw data into information through intelligent, automated work processes, which assist engineers with key well operations and monitoring, improving their productivity and decision-making. A major oil and gas operator in the Middle East is developing a set of intelligent workflows for key activities and processes for its production operations, with the ultimate goal of improved asset performance. Real-time surveillance and monitoring of production operation processes have proven to be operationally and economically important for managing complex, high-cost reservoirs. However, predicting short-term production and production interruptions—for example, related to pump settings—has posed a tremendous challenge. While operators routinely forecast production for the next 60 to 90 days, sophisticated tools such as full-field numerical simulation models are of limited use in predicting s...

A system of Water-Alternating-Gas (WAG) injection for enhanced oil recovery (Enhanced Oil Recover... more A system of Water-Alternating-Gas (WAG) injection for enhanced oil recovery (Enhanced Oil Recovery, EOR) comprises a mechanical drill hole, which is configured to permit selective multi-point injection of water and gas. The system also includes an optimization engine, which is designed for the calculation of reservoir fluid dynamics and for the selective injection of water and gas through the mechanical wellbore in accordance with the deposit of flow dynamics.

Offshore Technology Conference Brasil

SPE Kuwait Oil and Gas Show and Conference, 2013

Traditional reconciliation of geomodels with production data is one of the most laborious tasks i... more Traditional reconciliation of geomodels with production data is one of the most laborious tasks in reservoir engineering. The uncertainty associated with the great majority of model variables only adds to the overall complexity. This paper describes the conceptualization, implementation, and visualization characteristics of the multilevel assisted history matching (AHM) technique that captures inherent model uncertainty and allows for better quantification of production forecasts. The workflow is applied to history matching of the pilot area in a major, structurally complex Middle East (ME) carbonate reservoir. The simulation model combines 49 wells in five waterflood patterns to match 50 years of oil production and 12 years of water injection and to predict eight years of production. Initially, the reservoir model was calibrated to match oil production by modifying permeability and/or porosity at well locations and by fine-tuning rock-type properties and water saturation. The second level history match implemented two-stage Markov chain Monte Carlo (McMC) stochastic optimization to minimize the misfit in water cut on a well-by-well basis. The inversion process is dramatically accelerated by the efficient parameterization of permeability, constraining the proxy model using streamline-based sensitivities and using parallel and cluster computing. The optimal number of representative history-matched models was identified to capture the uncertainty in reservoir spatial connectivity using rigorous optimization and dynamic model ranking based on forecasted oil recovery factors (ORFs). The reduced set of models minimized the computational load for forecast-based analysis, while retaining the knowledge of the uncertainty in the recovery factor. The comprehensive probabilistic AHM workflow was implemented at the operator's North Kuwait Integrated Digital Oilfield (KwIDF) collaboration center. It delivers an optimized reservoir model for waterflood management and automatically updates the model quarterly with geological, production, and completion information. This allows engineers to improve the reservoir characterization and identify the areas that require more data capture.