Potential Benefits of Fluid Optimization for Combined Smart-Water and Polymer Flooding: Impact on Remaining Oil Saturation (Russian)

2019 ◽  
Author(s):  
Muhammad Tahir ◽  
Rafael E. Hincapie ◽  
Hendrik Foedisch ◽  
Gion-Joèl Strobel ◽  
Leonhard Ganzer
Keyword(s):  
Polymer Flooding ◽  
Oil Saturation ◽  
Smart Water ◽  
Remaining Oil ◽  
Potential Benefits ◽  
Fluid Optimization

Potential Benefits of Fluid Optimization for Combined Smart-Water and Polymer Flooding: Impact on Remaining Oil Saturation

2019 ◽  
Author(s):  
Muhammad Tahir ◽  
Rafael E. Hincapie ◽  
Hendrik Foedisch ◽  
Gion-Joèl Strobel ◽  
Leonhard Ganzer
Keyword(s):  
Polymer Flooding ◽  
Oil Saturation ◽  
Smart Water ◽  
Remaining Oil ◽  
Potential Benefits ◽  
Fluid Optimization

Wireline Log Interpretation Method of Remaining Oil Saturation in Polymer Flooding Reservoir

2013 ◽  
Vol 295-298 ◽  
pp. 3124-3128
Author(s):  
Ying Hua Yu ◽  
Qing Guo Zhang ◽  
Xiang Li Zhong
Keyword(s):  
Theoretical Analysis ◽  
Pore Structure ◽  
Physical Model ◽  
Polymer Flooding ◽  
Oil Exploration ◽  
Oil Saturation ◽  
Remaining Oil ◽  
Practical Development ◽  
Log Interpretation ◽  
Interpretation Method

Interpreting the remaining oil saturation in polymer flooding reservoir is very important for the oil exploration and development. Based on the original physical model of Archie formula, a kind of remaining oil saturation interpretation method for the polymer flooding reservoir is proposed, on the analysis of the variation rules of Archie parameters caused by the polymer influences in the reservoir microscopic pore structure, conductivity and wettability in this article. Theoretical analysis and actual applications show that Archie formula is still applied to calculate the remaining oil saturation for polymer flooding reservoir. This formula form is simple, easy to calculate the parameters, the interpretation accuracy can satisfy the practical development needs, and can be widely used for polymer flooding reservoir.

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

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.

scholarly journals SURFACTANT-POLYMER FLOODING PERFORMANCE IN HETEROGENEOUS TWO-LAYERED POROUS MEDIA

2011 ◽  
Vol 12 (1) ◽  
pp. 31-38 ◽  
Author(s):  
Muhammad Taufiq Fathaddin ◽  
Asri Nugrahanti ◽  
Putri Nurizatulshira Buang ◽  
Khaled Abdalla Elraies
Keyword(s):  
Porous Media ◽  
Oil Recovery ◽  
Polymer Flooding ◽  
Water Flooding ◽  
Residual Oil ◽  
Oil Saturation ◽  
Reservoir Heterogeneity ◽  
Residual Oil Saturation ◽  
Layered Porous Media ◽  
Multiple Porosity

In this paper, simulation study was conducted to investigate the effect of spatial heterogeneity of multiple porosity fields on oil recovery, residual oil and microemulsion saturation. The generated porosity fields were applied into UTCHEM for simulating surfactant-polymer flooding in heterogeneous two-layered porous media. From the analysis, surfactant-polymer flooding was more sensitive than water flooding to the spatial distribution of multiple porosity fields. Residual oil saturation in upper and lower layers after water and polymer flooding was about the same with the reservoir heterogeneity. On the other hand, residual oil saturation in the two layers after surfactant-polymer flooding became more unequal as surfactant concentration increased. Surfactant-polymer flooding had higher oil recovery than water and polymer flooding within the range studied. The variation of oil recovery due to the reservoir heterogeneity was under 9.2%.

Toward Quantitative Remaining Oil Saturation (ROS): Determination Challenges and Techniques

2011 ◽  
Author(s):  
Ahmed A. Al-harbi ◽  
Denis Philippe Schmitt ◽  
Shouxiang Mark Ma
Keyword(s):  
Oil Saturation ◽  
Remaining Oil
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