Gustavo Becerra - Academia.edu (original) (raw)

Uploads

Papers by Gustavo Becerra

Research paper thumbnail of Mitigação de incertezas atraves da integração com ajuste de historico de produção

The lack of reliable data or with high degree of uncertainty yields risk to the process of produc... more The lack of reliable data or with high degree of uncertainty yields risk to the process of production prediction making the history matching, the model calibration from the registered field production indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead acceptable solutions, especially whit high degree of uncertainty of these attributes. The integration of history matching with a probabilistic analysis of representative models yields a way to detect matched models inside an acceptance interval, providing more efficient framework for predictions. It is necessary to consider dependences between global and local attributes. The scope of this work is to present a methodology that integrates the uncertainty analysis with the history matching process in complex models. This procedure helps to detect critical subsurface attributes and their possible variation, in order to estimate a representative range of the additional reserves to be developed.. It is not an objective to obtain the best deterministic model, but to mitigate uncertainties by using observed data. The objective is to improve the methodology initiated by Moura Filho (2006), applied to a simple model. The methodology presented in this work is applied in two study cases with similar complexity. Firstly, the methodology is verified and validated, on global scale, in Namorado Field. Then, at the application stage, it is chosen a synthetic reservoir model made from real outcrop data of Brazil and involving information from analog fields with turbiditic systems deposited in deep waters. The methodology allows the redefinition of the probability and levels of the dynamic and static attributes in order: (1) to reduce the group of possible history matching obtaining more realistic models; (2) to identify the existent uncertainty as a function of observed data; (3) to decrease the uncertainty range of critical reservoir parameters; (4) to increase the confidence in production forecast. One contribution of this work is to present a quantitative approach to increase the reliability on the use of reservoir simulation as an auxiliary tool in decision processes. Another purpose of this work is to provide a procedure to reduce the consumed time to handle multiples uncertainty attributes during the history matching.

Research paper thumbnail of Petroleum reservoir uncertainty mitigation through the integration with production history matching

Journal of The Brazilian Society of Mechanical Sciences and Engineering, Jun 1, 2011

This paper presents a new methodology to deal with uncertainty mitigation using observed data, in... more This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.

Research paper thumbnail of A Methodology To Reduce Uncertainty Constrained to Observed Data

SPE Reservoir Evaluation & Engineering, 2009

Summary This paper presents a new method to deal with uncertainty mitigation by using observed da... more Summary This paper presents a new method to deal with uncertainty mitigation by using observed data, integrating the uncertainty analysis and the history-matching processes. The proposed methods are robust and easy to use, and offer an alternative to traditional history-matching methods. The main characteristic of the method is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The main objective is the integration of uncertainty analysis with history matching, providing a natural manner to make predictions under reduced uncertainty. Three methods are proposed: (1) probability redistribution, (2) elimination of attribute levels, and (3) redefinition of attribute values. To test the results of the proposed approach, we investigated three reservoir examples. The first one is a synthetic and simple case; the second one is a synthetic but realistic case; and the third one is a real reservoir from the Campos basin of Brazil. The results present...

Research paper thumbnail of Uncertainty History Matching and Forecasting, a Field Case Application

All Days, Apr 16, 2012

Geological, reservoir, economical and technological uncertainties have an effect on decision maki... more Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. A methodology that allows the recognition of well-calibrated models within an acceptable deviation is used. This procedure helps to identify the critical uncertain parameters and their possible variation in order to estimate the representative reserve range. The goal is not to find the best deterministic match, but rather to show how the calibration process allows a mitigation of identified uncertainties.A real case based on a reservoir from Campos Basin in Brazil was used. A 14 year historical period followed by a 12 year forecast period was considered, allowing verification and validation, at a global level, of the proposed procedure in a complex dynamic model. Two different commercial softwares were used, in order to demonstrate the advantages and restrictions of each approach. Distribution variations of the responses in time were evaluated by Latin Hypercube sampling and Monte Carlo propagation on validated proxy models.The proposed methodology allows: (1) to reduce the range of possible models taking into account the observed data; (2) to identify the existent uncertainty as a function of observed data; (3) to reduce the uncertainty range of critical reservoir parameters; (4) to increase confidence in production forecast. One contribution of this work is to present a quantitative approach for increasing the reliability of reservoir simulation as an auxiliary tool in decision making processes in order to reduce the associates risk and to maximize development opportunities.

Research paper thumbnail of Uncertainty History Matching and Forecasting, a Field Case Application

All Days, 2012

Geological, reservoir, economical and technological uncertainties have an effect on decision maki... more Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. ...

Research paper thumbnail of Petroleum reservoir uncertainty mitigation through the integration with production history matching

Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2011

This paper presents a new methodology to deal with uncertainty mitigation using observed data, in... more This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.

Research paper thumbnail of Mitigação de incertezas atraves da integração com ajuste de historico de produção

The lack of reliable data or with high degree of uncertainty yields risk to the process of produc... more The lack of reliable data or with high degree of uncertainty yields risk to the process of production prediction making the history matching, the model calibration from the registered field production indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead acceptable solutions, especially whit high degree of uncertainty of these attributes. The integration of history matching with a probabilistic analysis of representative models yields a way to detect matched models inside an acceptance interval, providing more efficient framework for predictions. It is necessary to consider dependences between global and local attributes. The scope of this work is to present a methodology that integrates the uncertainty analysis with the history matching process in complex models. This procedure helps to detect critical subsurface attributes and their possible variation, in order to estimate a representative range of the additional reserves to be developed.. It is not an objective to obtain the best deterministic model, but to mitigate uncertainties by using observed data. The objective is to improve the methodology initiated by Moura Filho (2006), applied to a simple model. The methodology presented in this work is applied in two study cases with similar complexity. Firstly, the methodology is verified and validated, on global scale, in Namorado Field. Then, at the application stage, it is chosen a synthetic reservoir model made from real outcrop data of Brazil and involving information from analog fields with turbiditic systems deposited in deep waters. The methodology allows the redefinition of the probability and levels of the dynamic and static attributes in order: (1) to reduce the group of possible history matching obtaining more realistic models; (2) to identify the existent uncertainty as a function of observed data; (3) to decrease the uncertainty range of critical reservoir parameters; (4) to increase the confidence in production forecast. One contribution of this work is to present a quantitative approach to increase the reliability on the use of reservoir simulation as an auxiliary tool in decision processes. Another purpose of this work is to provide a procedure to reduce the consumed time to handle multiples uncertainty attributes during the history matching.

Research paper thumbnail of Petroleum reservoir uncertainty mitigation through the integration with production history matching

Journal of The Brazilian Society of Mechanical Sciences and Engineering, Jun 1, 2011

This paper presents a new methodology to deal with uncertainty mitigation using observed data, in... more This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.

Research paper thumbnail of A Methodology To Reduce Uncertainty Constrained to Observed Data

SPE Reservoir Evaluation & Engineering, 2009

Summary This paper presents a new method to deal with uncertainty mitigation by using observed da... more Summary This paper presents a new method to deal with uncertainty mitigation by using observed data, integrating the uncertainty analysis and the history-matching processes. The proposed methods are robust and easy to use, and offer an alternative to traditional history-matching methods. The main characteristic of the method is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The main objective is the integration of uncertainty analysis with history matching, providing a natural manner to make predictions under reduced uncertainty. Three methods are proposed: (1) probability redistribution, (2) elimination of attribute levels, and (3) redefinition of attribute values. To test the results of the proposed approach, we investigated three reservoir examples. The first one is a synthetic and simple case; the second one is a synthetic but realistic case; and the third one is a real reservoir from the Campos basin of Brazil. The results present...

Research paper thumbnail of Uncertainty History Matching and Forecasting, a Field Case Application

All Days, Apr 16, 2012

Geological, reservoir, economical and technological uncertainties have an effect on decision maki... more Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. A methodology that allows the recognition of well-calibrated models within an acceptable deviation is used. This procedure helps to identify the critical uncertain parameters and their possible variation in order to estimate the representative reserve range. The goal is not to find the best deterministic match, but rather to show how the calibration process allows a mitigation of identified uncertainties.A real case based on a reservoir from Campos Basin in Brazil was used. A 14 year historical period followed by a 12 year forecast period was considered, allowing verification and validation, at a global level, of the proposed procedure in a complex dynamic model. Two different commercial softwares were used, in order to demonstrate the advantages and restrictions of each approach. Distribution variations of the responses in time were evaluated by Latin Hypercube sampling and Monte Carlo propagation on validated proxy models.The proposed methodology allows: (1) to reduce the range of possible models taking into account the observed data; (2) to identify the existent uncertainty as a function of observed data; (3) to reduce the uncertainty range of critical reservoir parameters; (4) to increase confidence in production forecast. One contribution of this work is to present a quantitative approach for increasing the reliability of reservoir simulation as an auxiliary tool in decision making processes in order to reduce the associates risk and to maximize development opportunities.

Research paper thumbnail of Uncertainty History Matching and Forecasting, a Field Case Application

All Days, 2012

Geological, reservoir, economical and technological uncertainties have an effect on decision maki... more Geological, reservoir, economical and technological uncertainties have an effect on decision making and consequently on reserves development plans. Quantifying the impact of these uncertainties can make this process more reliable. A great difficulty to achieve this in practice is the variability and complexity of workflows available to manage uncertainty using numerical simulation.The inaccuracy, high uncertainty or lack of reliable data yields risk to the forecasting process, making the calibration of the dynamical model with the field production data indispensable. History matching is an inverse problem and, in general, different combinations of reservoir attributes can lead to acceptable solutions, especially due to the high degree of uncertainty of these attributes. A set of solutions that respect the observed data may lead to different prediction scenarios.The objective of this work is the integration of history matching with probabilistic analysis of representative scenarios. ...

Research paper thumbnail of Petroleum reservoir uncertainty mitigation through the integration with production history matching

Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2011

This paper presents a new methodology to deal with uncertainty mitigation using observed data, in... more This paper presents a new methodology to deal with uncertainty mitigation using observed data, integrating the uncertainty analysis and the history matching processes. The proposed method is robust and easy to use, offering an alternative way to traditional history matching methodologies. The main characteristic of the methodology is the use of observed data as constraints to reduce the uncertainty of the reservoir parameters. The integration of uncertainty analysis with history matching naturally yields prediction under uncertainty. The workflow permits to establish a target range of uncertainty that characterize a confidence interval of the probabilistic distribution curves around the observed data. A complete workflow of the proposed methodology was carried out in a realistic model based on outcrop data and the impact of the uncertainty reduction in the production forecasting was evaluated. It was demonstrated that for complex cases, with a high number of uncertain attributes and several objective-function, the methodology can be applied in steps, beginning with a field analysis followed by regional and local (well level) analyses. The main contribution of this work is to provide an interesting way to quantify and to reduce uncertainties with the objective to generate reliable scenario-based models for consistent production prediction.