Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

View through CrossRef
Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.
Title: Oil Saturation Log Prediction Using Neural Network in New Steamflood Area
Description:
Surveillance is very important in managing a steamflood project.
On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years.
Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly.
Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection.
Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval.
The methodology that is used to predict oil saturation log is neural network.
In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input.
A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019.
Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model.
As the result of neural model testing, R2 is score 0.
86 with RMS 5% oil saturation.
In this testing step, oil saturation log prediction is compared to actual data.
Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match.
This neural network model is then used for oil saturation log prediction in 19 incomplete log set.
The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area.
This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Related Results

Relative Permeability Effects on the Migration of Steamflood Saturation Fronts
Relative Permeability Effects on the Migration of Steamflood Saturation Fronts
Abstract The effects of various relative permeability-saturation relationships on the movement of water saturation fronts during steamflooding is investigated. Em...
Tracking Thermal Saturation Fronts by a High Level PC Programming Language
Tracking Thermal Saturation Fronts by a High Level PC Programming Language
Abstract This paper presents a PC based alternative procedure for determining the water saturations within the hot water zone of a thermal project for use in anal...
Steam Distillation Studies For The Kern River Field
Steam Distillation Studies For The Kern River Field
Abstract The interactions of heavy oil and injected steam in the mature steamflood at the Kern River Field have been extensively studied to gain insight into the ...
Analysis of Scaled Steamflood Experiments
Analysis of Scaled Steamflood Experiments
Abstract Canada's heavy oil and oil sands deposits are estimated to contain as much oil as the conventional oil resources of the entire world. Heavy oil deposits,...
Machine Learning for Determining Remaining Oil Saturation Based On C/O Spectral Logging in Multilayer String Cased Well
Machine Learning for Determining Remaining Oil Saturation Based On C/O Spectral Logging in Multilayer String Cased Well
Dynamic monitoring of reservoir can reflect the physical response of fluid underground and clarify the oil and water distribution of the production, which is important for the prod...
Steam Mobility in Porous Media
Steam Mobility in Porous Media
American Institute of Mining, Metallurgical, and Petroleum Engineers, Inc. Abstract An experimental investigation was made to st...
Evaluation of Water-Oil Displacement Efficiency Using Subsurface Logs
Evaluation of Water-Oil Displacement Efficiency Using Subsurface Logs
Abstract A method is presented for the calculation of in situ values of oil-displacement efficiency by water flushing using conventional subsurface logs. The meth...
Adaptive hybrid potential evapotranspiration (PET) prediction method based on automatic machine learning
Adaptive hybrid potential evapotranspiration (PET) prediction method based on automatic machine learning
Abstract In arid areas, estimation of crop water demand through potential evapotranspiration (PET) forecast has a guiding effect on water-saving irrigation, to cope with th...

Back to Top