Pore-Level Network Modeling of Three-Phase Capillary Pressure and Relative Permeability Curves

1997 ◽  
Author(s):  
V. Mani ◽  
K.K. Mohanty
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Network Modeling ◽  
Three Phase ◽  
Pore Level ◽  
Relative Permeability Curves

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.

scholarly journals Uncertainty span for relative permeability and capillary pressure by varying wettability and spatial flow directions utilizing pore scale modelling

2020 ◽  
Vol 146 ◽  
pp. 01002
Author(s):  
Thomas Ramstad ◽  
Anders Kristoffersen ◽  
Einar Ebeltoft
Keyword(s):  
Continental Shelf ◽  
Pore Structure ◽  
Capillary Pressure ◽  
Relative Permeability ◽  
Phase Flow ◽  
Pore Scale ◽  
Full Field ◽  
Scale Modelling ◽  
Relative Permeability Curves ◽  
Norwegian Continental Shelf

Relative permeability and capillary pressure are key properties within special core analysis and provide crucial information for full field simulation models. These properties are traditionally obtained by multi-phase flow experiments, however pore scale modelling has during the last decade shown to add significant information as well as being less time-consuming to obtain. Pore scale modelling has been performed by using the lattice-Boltzmann method directly on the digital rock models obtained by high resolution micro-CT images on end-trims available when plugs are prepared for traditional SCAL-experiments. These digital rock models map the pore-structure and are used for direct simulations of two-phase flow to relative permeability curves. Various types of wettability conditions are introduced by a wettability map that opens for local variations of wettability on the pore space at the pore level. Focus have been to distribute realistic wettabilities representative for the Norwegian Continental Shelf which is experiencing weakly-wetting conditions and no strong preference neither to water nor oil. Spanning a realistic wettability-map and enabling flow in three directions, a large amount of relative permeability curves is obtained. The resulting relative permeabilities hence estimate the uncertainty of the obtained flow properties on a spatial but specific pore structure with varying, but realistic wettabilities. The obtained relative permeability curves are compared with results obtained by traditional SCAL-analysis on similar core material from the Norwegian Continental Shelf. The results are also compared with the SCAL-model provided for full field simulations for the same field. The results from the pore scale simulations are within the uncertainty span of the SCAL models, mimic the traditional SCAL-experiments and shows that pore scale modelling can provide a time- and cost-effective tool to provide SCAL-models with uncertainties.

Proxy models for evaluation of permeability, three-phase relative permeability, and capillary pressure curves from rate-transient data

SIMULATION ◽  
2019 ◽  
pp. 003754971985713 ◽  
Author(s):  
Zhenzihao Zhang ◽  
Turgay Ertekin
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Functional Relationship ◽  
Water Saturation ◽  
Petrophysical Properties ◽  
Laboratory Measurements ◽  
Connate Water ◽  
Three Phase ◽  
Transient Data ◽  
Critical Gas Saturation

This study developed a data-driven forecasting tool that predicts petrophysical properties from rate-transient data. Traditional estimations of petrophysical properties, such as relative permeability (RP) and capillary pressure (CP), strongly rely on coring and laboratory measurements. Coring and laboratory measurements are typically conducted only in a small fraction of wells. To contend with this constraint, in this study, we develop artificial neural network (ANN)-based tools that predict the three-phase RP relationship, CP relationship, and formation permeability in the horizontal and vertical directions using the production rate and pressure data for black-oil reservoirs. Petrophysical properties are related to rate-transient data as they govern the fluid flow in oil/gas reservoirs. An ANN has been proven capable of mimicking any functional relationship with a finite number of discontinuities. To generate an ANN representing the functional relationship between rate-transient data and petrophysical properties, an ANN structure pool is first generated and trained. Cases covering a wide spectrum of properties are then generated and put into training. Training of ANNs in the pool and comparisons among their performance yield the desired ANN structure that performs the most effectively among the ANNs in the pool. The developed tool is validated with blind tests and a synthetic field case. Reasonable predictions for the field cases are obtained. Within a fraction of second, the developed ANNs infer accurate characteristics of RP and CP for three phases as well as residual saturation, critical gas saturation, connate water saturation, and horizontal permeability with a small margin of error. The predicted RP and CP relationship can be generated and applied in history matching and reservoir modeling. Moreover, this tool can spare coring expenses and prolonged experiments in most of the field analysis. The developed ANNs predict the characteristics of three-phase RP and CP data, connate water saturation, residual oil saturation, and critical gas saturation using rate-transient data. For cases fulfilling the requirement of the tool, the proposed technique improves reservoir description while reducing expenses and time associated with coring and laboratory experiments at the same time.

Three-Phase Relative Permeability and Capillary Pressure Models With Hysteresis and Compositional Consistency

SPE Journal ◽  
2018 ◽  
Vol 23 (06) ◽  
pp. 2394-2408 ◽  
Author(s):  
Sajjad S. Neshat ◽  
Gary A. Pope
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
Numerical Stability ◽  
Physical Parameters ◽  
Gas Oil ◽  
Water Alternating Gas ◽  
Three Phase ◽  
Minimum Number ◽  
Trapping Number ◽  
New Formulation

Summary New coupled three-phase hysteretic relative permeability and capillary pressure models have been developed and tested for use in compositional reservoir simulators. The new formulation incorporates hysteresis and compositional consistency for both capillary pressure and relative permeability. This approach is completely unaffected by phase flipping and misidentification, which commonly occur in compositional simulations. Instead of using phase labels (gas/oil/solvent/aqueous) to define hysteretic relative permeability and capillary pressure parameters, the parameters are continuously interpolated between reference values using the Gibbs free energy (GFE) of each phase at each timestep. Models that are independent of phase labels have many advantages in terms of both numerical stability and physical consistency. The models integrate and unify relevant physical parameters, including hysteresis and trapping number, into one rigorous algorithm with a minimum number of parameters for application in numerical reservoir simulators. The robustness of the new models is demonstrated with simulations of the miscible water-alternating-gas (WAG) process and solvent stimulation to remove condensate and/or water blocks in both conventional and unconventional formations.

Simultaneous Interpretation of Three-Phase Relative Permeability and Capillary Pressure for a Tight Carbonate Reservoir From Wireline Formation Testing

2019 ◽  
Vol 142 (6) ◽  
Author(s):  
Xiangnan Liu ◽  
Daoyong Yang
Keyword(s):  
Capillary Pressure ◽  
Relative Permeability ◽  
History Matching ◽  
Carbonate Reservoir ◽  
Reservoir Rock ◽  
Fluid Saturation ◽  
Three Phase ◽  
Tight Carbonate ◽  
Wet Condition ◽  
Oil Gas

Abstract In this paper, techniques have been developed to interpret three-phase relative permeability and water–oil capillary pressure simultaneously in a tight carbonate reservoir from numerically simulating wireline formation tester (WFT) measurements. A high-resolution cylindrical near-wellbore model is built based on a set of pressures and flow rates collected by dual packer WFT in a tight carbonate reservoir. The grid quality is validated, the effective thickness of the WFT measurements is examined, and the effectiveness of the techniques is confirmed prior to performing history matching for both the measured pressure drawdown and buildup profiles. Water–oil relative permeability, oil–gas relative permeability, and water–oil capillary pressure are interpreted based on power-law functions and under the assumption of a water-wet reservoir and an oil-wet reservoir, respectively. Subsequently, three-phase relative permeability for the oil phase is determined using the modified Stone II model. Both the relative permeability and the capillary pressure of a water–oil system interpreted under an oil-wet condition match well with the measured relative permeability and capillary pressure of a similar reservoir rock type collected from the literature, while the relative permeability of an oil–gas system and the three-phase relative permeability bear a relatively high uncertainty. Not only is the reservoir determined as oil-wet but also the initial oil saturation is found to impose an impact on the interpreted water relative permeability under an oil-wet condition. Changes in water and oil viscosities and mud filtrate invasion depth affect the range of the movable fluid saturation of the interpreted water–oil relative permeabilities.

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