scholarly journals A New Method for Predicting the Permeability of Sandstone in Deep Reservoirs

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Feisheng Feng ◽  
Pan Wang ◽  
Zhen Wei ◽  
Guanghui Jiang ◽  
Dongjing Xu ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Capillary Pressure ◽  
Prediction Models ◽  
Reservoir Rock ◽  
Support Vector ◽  
Pressure Curve ◽  
Pore Throat ◽  
Capillary Pressure Curve ◽  
Mercury Injection ◽  
Mercury Injection Capillary Pressure

Capillary pressure curve data measured through the mercury injection method can accurately reflect the pore throat characteristics of reservoir rock; in this study, a new methodology is proposed to solve the aforementioned problem by virtue of the support vector regression tool and two improved models according to Swanson and capillary parachor parameters. Based on previous research data on the mercury injection capillary pressure (MICP) for two groups of core plugs excised, several permeability prediction models, including Swanson, improved Swanson, capillary parachor, improved capillary parachor, and support vector regression (SVR) models, are established to estimate the permeability. The results show that the SVR models are applicable in both high and relatively low porosity-permeability sandstone reservoirs; it can provide a higher degree of precision, and it is recognized as a helpful tool aimed at estimating the permeability in sandstone formations, particularly in situations where it is crucial to obtain a precise estimation value.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

Fractal Capillary Pressure Curve Models

Fractals ◽  
2017 ◽  
pp. 29-54
Author(s):  
Behzad Ghanbarian ◽  
Humberto Millán
Keyword(s):  
Capillary Pressure ◽  
Pressure Curve ◽  
Capillary Pressure Curve

A comparative study of permeability prediction for Eocene sandstones - Part 1: Application of modified Swanson models to MICP and NMR data

Geophysics ◽  
2021 ◽  
pp. 1-45
Author(s):  
Zeyu Zhang ◽  
Andreas Weller
Keyword(s):  
Capillary Pressure ◽  
Prediction Models ◽  
Characteristic Point ◽  
Single Point ◽  
Nmr Relaxation ◽  
Pressure Curve ◽  
Throat Radius ◽  
Predictive Quality ◽  
Nmr Data ◽  
Apex Point

The mercury injection capillary pressure (MICP) method and nuclear magnetic resonance (NMR) relaxometry provide insight into the pore radius distribution (PRD) either of pore throats (MICP) or pore bodies (NMR) of rocks. A variety of permeability (k) prediction models is based on the knowledge of the PRD. We evaluate the quality of k-prediction models using a sample set of Eocene sandstones with known values of measured permeability. The Swanson method relates the apex point of the capillary pressure curve to k. Although this widely acknowledged method uses only a single point of the PRD, it provides a predictive quality with an average ratio between measured and predicted permeability lower than a factor 3. The pore throat radius of the apex point proves to be a good proxy of the effective hydraulic radius. We demonstrate that an improved k prediction can be achieved if a larger section of the PRD is considered in the proposed generalized model. Using reliable values of surface relaxivity, the NMR relaxation time distribution is transformed into a PRD. We show that a characteristic apex point can be determined from NMR data, too. This characteristic point enables a good k prediction for the set of Eocene sandstone samples. In contrast to MICP, the predictive quality cannot be improved by applying an integration over a larger section of the PRD. Further test with samples of different pore structure and lithology should demonstrate the potential of the proposed models.

Characterization of Fluid Drainage Mechanism at Core and Pore Scales: an NMR Capillary Pressure–Based Saturation Exponent Prediction

2021 ◽  
Author(s):  
Abubakar Isah ◽  
Abdulrauf Rasheed Adebayo ◽  
Mohamed Mahmoud ◽  
Lamidi O. Babalola ◽  
Ammar El-Husseiny
Keyword(s):  
Electrical Resistivity ◽  
Capillary Pressure ◽  
Oil Recovery ◽  
Gas Storage ◽  
Pressure Curve ◽  
Pore Scale ◽  
Resistivity Index ◽  
Capillary Pressure Curve ◽  
In Series ◽  
Saturation Exponent

Abstract Capillary pressure (Pc) and electrical resistivity index (RI) curves are used in many reservoir engineering applications. Drainage capillary pressure curve represents a scenario where a non-wetting phase displaces a wetting phase such as (i) during gas injection (ii) gas storage in reservoirs (e.g. aquifer or depleted hydrocarbon reservoirs). The gas used for injection is typically natural gas, N2, or CO2. Gas storage principally used to meet requirement variations, and water injection into oil-wet reservoirs are drainage processes. Resistivity index (RI) curve which is used to evaluate the potential of oil recovery from a reservoir, is also an important tool used in log calibration and reservoir fluid typing. The pore drainage mechanism in a multimodal pore system is important for effective recovery of hydrocarbon reserves; enhance oil recovery (EOR) planning and underground gas storage. The understanding of pore structure and drainage mechanism within a multimodal pore system during petrophysical analysis is of paramount importance to reservoir engineers. Therefore, it becomes inherent to study and establish a way to relate these special core analyses laboratory (SCAL) methods with quick measurements such as the nuclear magnetic resonance (NMR) to reduce the time requirement for analysis. This research employed the use of nuclear magnetic resonance (NMR) to estimate saturation exponent (n) of rocks using nitrogen as the displacing fluid. Different rock types were used in this study that cover carbonates, sandstones, and dolomites. We developed an analytical workflow to separate the capillary pressure curve into capillary pressure curve for macropores and a capillary pressure curve for the micropores, and then used these pore scale Pc curves to estimate an NMR - capillary pressure - based electrical resistivity index - saturation (NMR-RI-Sw) curve for the rocks. We predicted the saturation exponent (n) for the rock samples from the NMR-RI-Sw curve. The NMR-based saturation exponent estimation method requires the transverse (T2) relaxation distribution of the rock - fluid system at various saturations. To verify the reliability of the new workflow, we performed porous plate capillary pressure and electrical resistivity measurements on the rock samples. The reliability of the results for the resistivity index curve and the saturation exponent was verified using the experimental data obtained from the SCAL method. The pore scale Pc curve was used to ascertain the drainage pattern and fluid contribution of the different pore subsystems. For bimodal rock system, the drainage mechanism can be in series, in parallel, or in series - parallel depending on the rock pore structure.

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