Oil Saturation in ShalesApplications in Seal Evaluation

1997 ◽  
Author(s):  
R. A. Noble ◽  
J. G. Kaldi ◽  
C. D. Atkinson
Keyword(s):  
Oil Saturation

Study of the causes for wax deposition under the operating conditions of main oil pipelines

2020 ◽  
Vol 10 (6) ◽  
pp. 610-619
Author(s):  
Rustam Z. Sunagatullin ◽  
◽  
Rinat M. Karimov ◽  
Radmir R. Tashbulatov ◽  
Boris N. Mastobaev ◽  
...  
Keyword(s):  
Phase Diagrams ◽  
Total Content ◽  
Operating Conditions ◽  
Wax Deposition ◽  
Oil Saturation ◽  
Oil Pipelines ◽  
Thermobaric Conditions ◽  
Wall Zone ◽  
Significant Factors ◽  
Commercial Oil

The results of investigations of the main causes and the most significant factors of intensification of paraffin deposition in main oil pipelines are presented. A comprehensive analysis of the composition and properties of commercial oils and their sediments was carried out, according to which phase diagrams of equilibrium of oil dispersed systems were obtained using the example of commercial oils from Bashkir fields. Based on the phase diagrams, a curve of wax oil saturation was constructed, the analysis of which confirms that the existing thermobaric conditions during the operation of main oil pipelines do not allow transporting oil without the risk of waxing. It was noted a special influence of the value of the temperature gradient in the near-wall zone and the imbalance of the ratio of high-molecular oil components in commercial batches formed in the process of joint pumping on the intensity of waxing of sections of oil pipelines complicated by deposits, which was confirmed by statistical data on the frequency of pigging. The regularities obtained in this way are proposed to be used as an express method for predicting complications associated with intensive waxing of main oil pipelines. In order to quickly assess the risks of waxing of sections of main oil pipelines, an indicator is introduced that characterizes the ratio of the content of solid paraffins to the total content of resins and asphaltenes of oil, called the criterion of instability of a commercial oil batch.

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 Ensemble Machine Learning Assisted Reservoir Characterization Using Field Production Data–An Offshore Field Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1052
Author(s):  
Baozhong Wang ◽  
Jyotsna Sharma ◽  
Jianhua Chen ◽  
Patricia Persaud
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Reservoir Characterization ◽  
Time Lapse ◽  
Production Data ◽  
Oil Saturation ◽  
Ensemble Machine Learning ◽  
Input Parameters ◽  
Saturation Profiles ◽  
Field Production

Estimation of fluid saturation is an important step in dynamic reservoir characterization. Machine learning techniques have been increasingly used in recent years for reservoir saturation prediction workflows. However, most of these studies require input parameters derived from cores, petrophysical logs, or seismic data, which may not always be readily available. Additionally, very few studies incorporate the production data, which is an important reflection of the dynamic reservoir properties and also typically the most frequently and reliably measured quantity throughout the life of a field. In this research, the random forest ensemble machine learning algorithm is implemented that uses the field-wide production and injection data (both measured at the surface) as the only input parameters to predict the time-lapse oil saturation profiles at well locations. The algorithm is optimized using feature selection based on feature importance score and Pearson correlation coefficient, in combination with geophysical domain-knowledge. The workflow is demonstrated using the actual field data from a structurally complex, heterogeneous, and heavily faulted offshore reservoir. The random forest model captures the trends from three and a half years of historical field production, injection, and simulated saturation data to predict future time-lapse oil saturation profiles at four deviated well locations with over 90% R-square, less than 6% Root Mean Square Error, and less than 7% Mean Absolute Percentage Error, in each case.

scholarly journals Photon-magnon response to fluid variability and saturation levels in sandstone reservoirs

2020 ◽  
Vol 17 (6) ◽  
pp. 1065-1074
Author(s):  
Abdullah Musa Ali ◽  
Amir Rostami ◽  
Noorhana Yahya
Keyword(s):  
Oil Recovery ◽  
Relative Permittivity ◽  
Wave Interaction ◽  
Heterogeneous Porous Media ◽  
Injection System ◽  
Bragg Grating ◽  
Fibre Bragg Grating ◽  
Core Flooding ◽  
Oil Saturation ◽  
Fluid Saturation

Abstract The need to recover high viscosity heavy oil from the residual phase of reservoirs has raised interest in the use of electromagnetics (EM) for enhanced oil recovery. However, the transformation of EM wave properties must be taken into consideration with respect to the dynamic interaction between fluid and solid phases. Consequently, this study discretises EM wave interaction with heterogeneous porous media (sandstones) under different fluid saturations (oil and water) to aid the monitoring of fluid mobility and activation of magnetic nanofluid in the reservoir. To achieve this aim, this study defined the various EM responses and signatures for brine and oil saturation and fluid saturation levels. A Nanofluid Electromagnetic Injection System (NES) was deployed for a fluid injection/core-flooding experiment. Inductance, resistance and capacitance (LRC) were recorded as the different fluids were injected into a 1.0-m long Berea core, starting from brine imbibition to oil saturation, brine flooding and eventually magnetite nanofluid flooding. The fluid mobility was monitored using a fibre Bragg grating sensor. The experimental measurements of the relative permittivity of the Berea sandstone core (with embedded detectors) saturated with brine, oil and magnetite nanofluid were given in the frequency band of 200 kHz. The behaviour of relative permittivity and attenuation of the EM wave was observed to be convolutedly dependent on the sandstone saturation history. The fibre Bragg Grating (FBG) sensor was able to detect the interaction of the Fe3O4 nanofluid with the magnetic field, which underpins the fluid mobility fundamentals that resulted in an anomalous response.

A New Method of Fall-Off Test in Water Injection Well in Tahe Oilfield

2013 ◽  
Vol 807-809 ◽  
pp. 2508-2513
Author(s):  
Qiang Wang ◽  
Wan Long Huang ◽  
Hai Min Xu
Keyword(s):  
Water Injection ◽  
Injection Well ◽  
Well Test ◽  
Well Bore ◽  
Residual Oil ◽  
Injection Wells ◽  
Oil Saturation ◽  
Tahe Oilfield ◽  
Fitting Method ◽  
Front Radius

In pressure drop well test of the clasolite water injection well of Tahe oilfield, through nonlinear automatic fitting method in the multi-complex reservoir mode for water injection wells, we got layer permeability, skin factor, well bore storage coefficient and flood front radius, and then we calculated the residual oil saturation distribution. Through the examples of the four wells of Tahe oilfield analyzed by our software, we found that the method is one of the most powerful analysis tools.

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