Interwell connectivities are fundamental parameters required to manage waterfloods in oil reservoirs. Data-driven models, such as the capacitance-resistance model （CRM）, are fast tools to estimate these parameters f...Interwell connectivities are fundamental parameters required to manage waterfloods in oil reservoirs. Data-driven models, such as the capacitance-resistance model （CRM）, are fast tools to estimate these parameters from time-correlations of input （injection rates） and output （production rates） signals. Noise and structure of the input time-series impose limits on the information that can be extracted from a given data-set. This work uses the CRM to study general prescriptions for the design of input signals that enhance the information content of injection/production data in the estimation of well-to-well interactions. Numerical schemes and general features of the optimal input signal strategy are derived for this problem.展开更多
Consequences of decommissioning oil fields on artisanal fishing activities are still little known in the literature. This paper is intended to shed some light on a process of dismantling and sinking of oil and gas str...Consequences of decommissioning oil fields on artisanal fishing activities are still little known in the literature. This paper is intended to shed some light on a process of dismantling and sinking of oil and gas structures in shallow waters, with severe disturbing impacts on low income artisanal fishing activities. From a socio-economic perspective, the relationship of oil industry with local communities is described, with the main perceived problems pointed out in local fishermen leadership perspective. The notions of ＂damages＂ and ＂mitigation＂ used by the oil industry are discussed in connection to the expansion and dismantling of oil installations during the past 20 yrs. A comparative view of oil fields decommissioning in Europe and Brazil during the late 1990s suggests the need to review transparency and social commitment standards which have been far less prominent in this Brazilian case. The authors believe that the Brazilian oil industry has acquired a social and environmental debt towards the whole society, as far as it has been unable to establish a clear and effective process for decommissioning their oil installations within the artisanal fishing areas of the Todosos Santos Bay. Furthermore, the discussion of fair and specific compensations has been avoided, which otherwise would be instrumental to regain local economic conditions found among fishermen just few decades ago.展开更多
Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider exte...Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider extended duration of well shut-ins(i.e.temporarily stopping oil production)for economic reasons.For example,prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells.In the case of partial shut-in,selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved.In this study,a mature oil field with a long(50+years)production history with 170+wells is considered.Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates.We aimed to solve this decision-making problem through unsupervised machine learning.Average reservoir characteristics at well locations,well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells.While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir,well performance consists of volumetric rates and pressures,which are frequently measured during oil production.After a multivariate data analysis that explored correlations among parameters,clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features.Using the field’s reservoir simulation model,scenarios of shutting in different groups of wells were simulated.Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to$30/bbl for 6,12 or 18 months.Results of economic analysis were analyzed to identify which group(s)of wells should have been shut-in by also considering the sensitivity to different price levels.It was observed that wells can be characterized in the 3-cluster case as low,medium and high performance wells.Analyzing the forecasting scenarios showed that shutting in all or high-and medium-performance wells altogether results in better economic outcomes.The results were most sensitive to the number of active wells and the oil price during the high-price period.This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes.Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods.This approach would lead to cost savings especially in mature fields with low-profit margins.展开更多
基金financial support and to the Center for Petroleum Asset Risk Management of the University of Texas at Austin for hospitality and an exciting research environment
文摘Interwell connectivities are fundamental parameters required to manage waterfloods in oil reservoirs. Data-driven models, such as the capacitance-resistance model （CRM）, are fast tools to estimate these parameters from time-correlations of input （injection rates） and output （production rates） signals. Noise and structure of the input time-series impose limits on the information that can be extracted from a given data-set. This work uses the CRM to study general prescriptions for the design of input signals that enhance the information content of injection/production data in the estimation of well-to-well interactions. Numerical schemes and general features of the optimal input signal strategy are derived for this problem.
文摘Consequences of decommissioning oil fields on artisanal fishing activities are still little known in the literature. This paper is intended to shed some light on a process of dismantling and sinking of oil and gas structures in shallow waters, with severe disturbing impacts on low income artisanal fishing activities. From a socio-economic perspective, the relationship of oil industry with local communities is described, with the main perceived problems pointed out in local fishermen leadership perspective. The notions of ＂damages＂ and ＂mitigation＂ used by the oil industry are discussed in connection to the expansion and dismantling of oil installations during the past 20 yrs. A comparative view of oil fields decommissioning in Europe and Brazil during the late 1990s suggests the need to review transparency and social commitment standards which have been far less prominent in this Brazilian case. The authors believe that the Brazilian oil industry has acquired a social and environmental debt towards the whole society, as far as it has been unable to establish a clear and effective process for decommissioning their oil installations within the artisanal fishing areas of the Todosos Santos Bay. Furthermore, the discussion of fair and specific compensations has been avoided, which otherwise would be instrumental to regain local economic conditions found among fishermen just few decades ago.
基金support from research grants MGA-2021-42991 and MYL-2022-43726,funded by Istanbul Technical University-Scientific Research Projects,Turkey.Thissupportis gratefully acknowledged.
文摘Fluctuations in oil prices adversely affect decision making situations in which performance forecasting must be combined with realistic price forecasts.In periods of significant price drops,companies may consider extended duration of well shut-ins(i.e.temporarily stopping oil production)for economic reasons.For example,prices during the early days of the Covid-19 pandemic forced operators to consider shutting in all or some of their active wells.In the case of partial shut-in,selection of candidate wells may evolve as a challenging decision problem considering the uncertainties involved.In this study,a mature oil field with a long(50+years)production history with 170+wells is considered.Reservoirs with similar conditions face many challenges related to economic sustainability such as frequent maintenance requirements and low production rates.We aimed to solve this decision-making problem through unsupervised machine learning.Average reservoir characteristics at well locations,well production performance statistics and well locations are used as potential features that could characterize similarities and differences among wells.While reservoir characteristics are measured at well locations for the purpose of describing the subsurface reservoir,well performance consists of volumetric rates and pressures,which are frequently measured during oil production.After a multivariate data analysis that explored correlations among parameters,clustering algorithms were used to identify groups of wells that are similar with respect to aforementioned features.Using the field’s reservoir simulation model,scenarios of shutting in different groups of wells were simulated.Forecasted reservoir performance for three years was used for economic evaluation that assumed an oil price drop to$30/bbl for 6,12 or 18 months.Results of economic analysis were analyzed to identify which group(s)of wells should have been shut-in by also considering the sensitivity to different price levels.It was observed that wells can be characterized in the 3-cluster case as low,medium and high performance wells.Analyzing the forecasting scenarios showed that shutting in all or high-and medium-performance wells altogether results in better economic outcomes.The results were most sensitive to the number of active wells and the oil price during the high-price period.This study demonstrated the effectiveness of unsupervised machine learning in well classification for operational decision making purposes.Operating companies may use this approach for improved decision making to select wells for extended shut-in during low oil-price periods.This approach would lead to cost savings especially in mature fields with low-profit margins.
