Understanding Formation Damage Processes: An Essential Ingredient For Improved Measurement And Interpretation Of Relative Permeability Data

1987 ◽  
Author(s):  
Jude O. Amaefule ◽  
A. Ajufo ◽  
E. Peterson ◽  
K. Durst
Keyword(s):  
Relative Permeability ◽  
Formation Damage ◽  
Essential Ingredient ◽  
Damage Processes ◽  
Permeability Data

A Machine Learning Methodology for Rock-Typing Using Relative Permeability Curves

2021 ◽  
Author(s):  
Carlos Esteban Alfonso ◽  
Frédérique Fournier ◽  
Victor Alcobia
Keyword(s):  
Machine Learning ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Fractional Flow ◽  
Flow Curves ◽  
Rock Types ◽  
Rock Typing ◽  
Relative Permeability Curves ◽  
Permeability Data

Abstract The determination of the petrophysical rock-types often lacks the inclusion of measured multiphase flow properties as the relative permeability curves. This is either the consequence of a limited number of SCAL relative permeability experiments, or due to the difficulty of linking the relative permeability characteristics to standard rock-types stemming from porosity, permeability and capillary pressure. However, as soon as the number of relative permeability curves is significant, they can be processed under the machine learning methodology stated by this paper. The process leads to an automatic definition of relative permeability based rock-types, from a precise and objective characterization of the curve shapes, which would not be achieved with a manual process. It improves the characterization of petrophysical rock-types, prior to their use in static and dynamic modeling. The machine learning approach analyzes the shapes of curves for their automatic classification. It develops a pattern recognition process combining the use of principal component analysis with a non-supervised clustering scheme. Before this, the set of relative permeability curves are pre-processed (normalization with the integration of irreducible water and residual oil saturations for the SCAL relative permeability samples from an imbibition experiment) and integrated under fractional flow curves. Fractional flow curves proved to be an effective way to unify the relative permeability of the two fluid phases, in a unique curve that characterizes the specific poral efficiency displacement of this rock sample. The methodology has been tested in a real data set from a carbonate reservoir having a significant number of relative permeability curves available for the study, in addition to capillary pressure, porosity and permeability data. The results evidenced the successful grouping of the relative permeability samples, according to their fractional flow curves, which allowed the classification of the rocks from poor to best displacement efficiency. This demonstrates the feasibility of the machine learning process for defining automatically rock-types from relative permeability data. The fractional flow rock-types were compared to rock-types obtained from capillary pressure analysis. The results indicated a lack of correspondence between the two series of rock-types, which testifies the additional information brought by the relative permeability data in a rock-typing study. Our results also expose the importance of having good quality SCAL experiments, with an accurate characterization of the saturation end-points, which are used for the normalization of the curves, and a consistent sampling for both capillary pressure and relative permeability measurements.

Artificial-Intelligence Technology Predicts Relative Permeability of Giant Carbonate Reservoirs

2009 ◽  
Vol 12 (01) ◽  
pp. 96-103 ◽  
Author(s):  
Saud M. Al-Fattah ◽  
Hamad A. Al-Naim
Keyword(s):  
Experimental Data ◽  
Relative Permeability ◽  
Reservoir Simulation ◽  
Nonlinear Problems ◽  
Carbonate Reservoir ◽  
Carbonate Reservoirs ◽  
Porous Rocks ◽  
Data Set ◽  
Ann Models ◽  
Permeability Data

Summary Determination of relative permeability data is required for almost all calculations of fluid flow in petroleum reservoirs. Water/oil relative permeability data play important roles in characterizing the simultaneous two-phase flow in porous rocks and predicting the performance of immiscible displacement processes in oil reservoirs. They are used, among other applications, for determining fluid distributions and residual saturations, predicting future reservoir performance, and estimating ultimate recovery. Undoubtedly, these data are considered probably the most valuable information required in reservoir simulation studies. Estimates of relative permeability are generally obtained from laboratory experiments with reservoir core samples. In the absence of the laboratory measurement of relative permeability data, developing empirical correlations for obtaining accurate estimates of relative permeability data showed limited success, and proved difficult, especially for carbonate reservoir rocks. Artificial-neural-network (ANN) technology has proved successful and useful in solving complex structured and nonlinear problems. This paper presents a new modeling technology to predict accurately water/oil relative permeability using ANN. The ANN models of relative permeability were developed using experimental data from waterflood-core-tests samples collected from carbonate reservoirs of giant Saudi Arabian oil fields. Three groups of data sets were used for training, verification, and testing the ANN models. Analysis of results of the testing data set show excellent agreement with the experimental data of relative permeability. In addition, error analyses show that the ANN models developed in this study outperform all published correlations. The benefits of this work include meeting the increased demand for conducting special core analysis (SCAL), optimizing the number of laboratory measurements, integrating into reservoir simulation and reservoir management studies, and providing significant cost savings on extensive lab work and substantial required time.

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