Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon...Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon plays where biogenic gases are dominant.This study integrates organic geochemistry,seismic geomorphology and petrophysics in order to decipher the origin,and accumulation conditions of the wet gas/condensate blend in the Upper Miocene sub-salt Wakar Formation sandstones in Port Fouad Marine“PFM”Field,offshore Nile Delta.Hydrocarbon pay zones are scattered thin(<10 m)sandstones deposited in as turbiditic channel/levee complex facies.Spatial distribution of vertical gas chimneys(~2 km wide)rooting-down to the Messinian Rosetta salt is associated with the lateral pinching-out of the turbiditic sandstones.Organically-rich(total organic carbon“TOC”>1 w.t.%,hydrogen index“HI”>200 mgHC/gTOC)and mature(Tmax>430℃,vitrinite reflectance“VR”>0.6%R_(o)),source rocks are restricted to Upper Miocene Wakar and Oligo-Miocene Tineh formations.The latter contains more mature organofacies(up to 1.2%R_(o))and type Ⅱ/Ⅲ kerogen,thereby demonstrating a good capability to generate wet gases.The studied gas is wet and has thermogenic origin with signs of secondary microbial alteration,whereas the condensate contains a mixture of marine and terrestrial input.Molecular bio-markers of the condensate,isotopic and molecular composition of the gas reveals a generation of condensate prior to gas expulsion from the source.The Wakar sandstones have a heterogeneous pore system where three reservoir rock types(RRTⅠ,RRTⅡ and RRTⅢ).RRTI rocks present the bulk compo-sition of the Wakar pay zones.Spatial distribution of RRTⅠ facies likely control the accumulation of the sub-salt hydrocarbons.Our results provide a new evidence on an active petroleum system in the sub-salt Paleogene successions in the offshore Nile Delta where concomitant generation of gas/condensate blend has been outlined.展开更多
Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and d...Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure measurements.However,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells.Applying machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited.In this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New Zealand.Their predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)model.The geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four wells.All ML methods achieved credible levels of pore pressure prediction performance.The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four wells.The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores.For two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 psi.The results suggest that these models can be used to predict pore pressure in nearby locations,i.e.similar geology at corresponding depths within a field,but they become less reliable as the step-out distance increases and geological conditions change significantly.In comparison to other approaches to predict pore pressures,this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.展开更多
文摘Offshore Nile Delta in Egypt represents an enormous hydrocarbon province with recent projected gas and condensate discoveries of more than 50 trillion cubic feet“TCF”.Most of these occur in the post-salt hydrocarbon plays where biogenic gases are dominant.This study integrates organic geochemistry,seismic geomorphology and petrophysics in order to decipher the origin,and accumulation conditions of the wet gas/condensate blend in the Upper Miocene sub-salt Wakar Formation sandstones in Port Fouad Marine“PFM”Field,offshore Nile Delta.Hydrocarbon pay zones are scattered thin(<10 m)sandstones deposited in as turbiditic channel/levee complex facies.Spatial distribution of vertical gas chimneys(~2 km wide)rooting-down to the Messinian Rosetta salt is associated with the lateral pinching-out of the turbiditic sandstones.Organically-rich(total organic carbon“TOC”>1 w.t.%,hydrogen index“HI”>200 mgHC/gTOC)and mature(Tmax>430℃,vitrinite reflectance“VR”>0.6%R_(o)),source rocks are restricted to Upper Miocene Wakar and Oligo-Miocene Tineh formations.The latter contains more mature organofacies(up to 1.2%R_(o))and type Ⅱ/Ⅲ kerogen,thereby demonstrating a good capability to generate wet gases.The studied gas is wet and has thermogenic origin with signs of secondary microbial alteration,whereas the condensate contains a mixture of marine and terrestrial input.Molecular bio-markers of the condensate,isotopic and molecular composition of the gas reveals a generation of condensate prior to gas expulsion from the source.The Wakar sandstones have a heterogeneous pore system where three reservoir rock types(RRTⅠ,RRTⅡ and RRTⅢ).RRTI rocks present the bulk compo-sition of the Wakar pay zones.Spatial distribution of RRTⅠ facies likely control the accumulation of the sub-salt hydrocarbons.Our results provide a new evidence on an active petroleum system in the sub-salt Paleogene successions in the offshore Nile Delta where concomitant generation of gas/condensate blend has been outlined.
文摘Pore pressure is an essential parameter for establishing reservoir conditions,geological interpretation and drilling programs.Pore pressure prediction depends on information from various geophysical logs,seismic,and direct down-hole pressure measurements.However,a level of uncertainty accompanies the prediction of pore pressure because insufficient information is usually recorded in many wells.Applying machine learning(ML)algorithms can decrease the level of uncertainty of pore pressure prediction uncertainty in cases where available information is limited.In this research,several ML techniques are applied to predict pore pressure through the over-pressured Eocene reservoir section penetrated by four wells in the Mangahewa gas field,New Zealand.Their predictions substantially outperform,in terms of prediction performance,those generated using a multiple linear regression(MLR)model.The geophysical logs used as input variables are sonic,temperature and density logs,and some direct pore pressure measurements were available at the reservoir level to calibrate the predictions.A total of 25,935 data records involving six well-log input variables were evaluated across the four wells.All ML methods achieved credible levels of pore pressure prediction performance.The most accurate models for predicting pore pressure in individual wells on a supervised basis are decision tree(DT),adaboost(ADA),random forest(RF)and transparent open box(TOB).The DT achieved root mean square error(RMSE)ranging from 0.25 psi to 14.71 psi for the four wells.The trained models were less accurate when deployed on a semi-supervised basis to predict pore pressure in the other wellbores.For two wells(Mangahewa-03 and Mangahewa-06),semi-supervised prediction achieved acceptable prediction performance of RMSE of 130—140 psi;while for the other wells,semi-supervised prediction performance was reduced to RMSE>300 psi.The results suggest that these models can be used to predict pore pressure in nearby locations,i.e.similar geology at corresponding depths within a field,but they become less reliable as the step-out distance increases and geological conditions change significantly.In comparison to other approaches to predict pore pressures,this study has identified that application of several ML algorithms involving a large number of data records can lead to more accurate prediction results.
