Enhanced Assessment of Fluid Saturation in the Wolfcamp Formation of the Permian Basin

2021 ◽  
Vol 62 (6) ◽  
pp. 737-751
Author(s):  
Sabyasachi Dash ◽  
◽  
Zoya Heidari ◽  
Keyword(s):  
Spatial Distribution ◽  
Water Saturation ◽  
Geometric Model ◽  
New Method ◽  
Model Parameters ◽  
Unique Contribution ◽  
Permian Basin ◽  
Resistivity Measurements ◽  
Fluid Saturation ◽  
Resistivity Model

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. They also do not reliably assimilate the spatial distribution of the clay network and pore structure. Moreover, they do not incorporate other conductive minerals and organic matter, impacting the resistivity measurements and leading to uncertainty in water saturation assessment. We recently introduced a resistivity-based model that quantitatively assimilates the type and spatial distribution of all rock constituents to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to expand the application of this model for well-log-based assessment of water/hydrocarbon saturation and to verify the reliability of the introduced method in the Wolfcamp Formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and nonconductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we developed an inversion algorithm with two objectives: (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. The geometric model parameters are determined for each rock type or formation by minimizing the difference between the measured resistivity and the resistivity estimated from pore combination modeling. We applied the new method to two wells drilled in the Wolfcamp Formation of the Permian Basin. The formation-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 24.1% and 32.4% compared to Archie’s and Waxman-Smits models, respectively, in the Wolfcamp Formation. The most considerable improvement was observed in the Middle and the Lower Wolfcamp Formations, where the average clay concentration was relatively higher than the other zones. There was an additional 70,000 bbl/acre of hydrocarbon reserve using the proposed method compared to when water saturation was quantified using Archie’s model in the Permian Basin, which is a 33% relative improvement. It should be highlighted that the new method did not require any calibration effort using core water saturation measurements, which is a unique contribution of this rock-physics-based workflow.

ENHANCED ASSESSMENT OF FLUID SATURATION IN THE WOLFCAMP FORMATION OF THE PERMIAN BASIN

2021 ◽  
Author(s):  
Sabyasachi Dash ◽  
◽  
Zoya Heidari ◽  
Keyword(s):  
Water Saturation ◽  
Geometric Model ◽  
Rock Physics ◽  
Model Parameters ◽  
Permian Basin ◽  
Resistivity Measurements ◽  
Clay Concentration ◽  
Resistivity Model ◽  
Shaly Sand ◽  
Hydrocarbon Reserves

Conventional resistivity models often overestimate water saturation in organic-rich mudrocks and require extensive calibration efforts. Conventional resistivity-porosity-saturation models assume brine in the formation as the only conductive component contributing to resistivity measurements. Enhanced resistivity models for shaly-sand analysis include clay concentration and clay-bound water as contributors to electrical conductivity. These shaly-sand models, however, consider the existing clay in the rock as dispersed, laminated, or structural, which does not reliably describe the distribution of clay network in organic-rich mudrocks. They also do not incorporate other conductive minerals and organic matter, which can significantly impact the resistivity measurements and lead to uncertainty in water saturation assessment. We recently introduced a method that quantitatively assimilates the type and spatial distribution of all conductive components to improve reserves evaluation in organic-rich mudrocks using electrical resistivity measurements. This paper aims to verify the reliability of the introduced method for the assessment of water/hydrocarbon saturation in the Wolfcamp formation of the Permian Basin. Our recently introduced resistivity model uses pore combination modeling to incorporate conductive (clay, pyrite, kerogen, brine) and non-conductive (grains, hydrocarbon) components in estimating effective resistivity. The inputs to the model are volumetric concentrations of minerals, the conductivity of rock components, and porosity obtained from laboratory measurements or interpretation of well logs. Geometric model parameters are also critical inputs to the model. To simultaneously estimate the geometric model parameters and water saturation, we develop two inversion algorithms (a) to estimate the geometric model parameters as inputs to the new resistivity model and (b) to estimate the water saturation. Rock type, pore structure, and spatial distribution of rock components affect geometric model parameters. Therefore, dividing the formation into reliable petrophysical zones is an essential step in this method. The geometric model parameters are determined for each rock type by minimizing the difference between the measured resistivity and the resistivity, estimated from Pore Combination Modeling. We applied the new rock physics model to two wells drilled in the Permian Basin. The depth interval of interest was located in the Wolfcamp formation. The rock-class-based inversion showed variation in geometric model parameters, which improved the assessment of water saturation. Results demonstrated that the new method improved water saturation estimates by 32.1% and 36.2% compared to Waxman-Smits and Archie's models, respectively, in the Wolfcamp formation. The most considerable improvement was observed in the Middle and Lower Wolfcamp formation, where the average clay concentration was relatively higher than the other zones. Results demonstrated that the proposed method was shown to improve the estimates of hydrocarbon reserves in the Permian Basin by 33%. The hydrocarbon reserves were underestimated by an average of 70000 bbl/acre when water saturation was quantified using Archie's model in the Permian Basin. It should be highlighted that the new method did not require any calibration effort to obtain model parameters for estimating water saturation. This method minimizes the need for extensive calibration efforts for the assessment of hydrocarbon/water saturation in organic-rich mudrocks. By minimizing the need for extensive calibration work, we can reduce the number of core samples acquired. This is the unique contribution of this rock-physics-based workflow.

APPLICATION OF IMAGE LOGS FOR ENHANCED RESISTIVITY-BASED WATER SATURATION ASSESSMENT IN ORGANIC-RICH MUDROCKS

2021 ◽  
Author(s):  
Sabyasachi Dash ◽  
◽  
Zoya Heidari ◽  
Keyword(s):  
Spatial Distribution ◽  
Water Saturation ◽  
Geometric Model ◽  
Model Parameters ◽  
Permian Basin ◽  
Volumetric Concentration ◽  
Rock Fabric ◽  
Resistivity Model ◽  
Joint Interpretation ◽  
Resistivity Image

Organic-rich mudrocks are complex in terms of rock fabric (i.e., the spatial distribution of rock components), which impacts electrical resistivity measurements and, therefore, estimates of hydrocarbon reserves. Conventional resistivity-saturation-porosity methods for assessment of water/hydrocarbon saturation do not reliably incorporate the spatial distribution of rock components and pores in the assessment of fluid saturation. Extensive calibration efforts are required for indirectly projecting the impact of rock fabric on resistivity models. For instance, none of the existing shaly-sand models incorporate a realistic distribution of clay network. This might be acceptable in conventional reservoirs. However, oversimplifying assumptions can cause significant uncertainty in reserves evaluation in organic-rich mudrocks. It should be noted that even the methods which incorporate the realistic distribution of rock components are difficult to calibrate. To address the aforementioned challenge, we introduce a joint interpretation of conventional resistivity and resistivity image logs to improve water saturation assessment by honoring the type of rock component, the spatial distribution of the conductive and non-conductive rock components, and the volumetric concentration of fluids and minerals in the rock. Borehole image logs are a source of high-resolution continuous rock sequence records and can provide detailed rock-fabric-related features. In this paper, we propose a method for the estimation of lamination density and mean resistivity value from image logs within each rock type. These fabric-related features are used to quantify the geometric model parameters for each conductive component of the rock. We use these geometric model parameters as inputs to a new resistivity model that considers volumetric concentration and spatial distribution of rock components for a depth-by-depth assessment of water saturation. The other inputs to the workflow are the volumetric concentration of conductive and non-conductive rock components, electrical conductivity of rock components, and porosity estimates from the joint interpretation of well logs. We successfully applied the proposed workflow to a dataset from the Wolfcamp formation in the Permian Basin in which resistivity image logs were available. We observed a measurable variation in estimated image-log-based geometric model parameters, which were in agreement with the visual content of the images. Incorporation of the estimated rock-class-based geometric model parameters in the resistivity model improved water saturation assessment. Results demonstrated a relative improvement in water saturation estimates of 44.2% and 59.1% against Waxman-Smits and Archie's models, respectively. We then used the estimated geometric model parameters for each rock type for a depth-by-depth assessment of water saturation in one additional well without image logs. This led to a faster and more reliable assessment of water saturation within a certain distance from the well with image logs, where the rock types remain comparable. This distance can be evaluated using variogram analysis. We demonstrated that using the estimated geometric model parameters could improve estimates of hydrocarbon reserves in the Permian Basin by approximately 34%. It should be noted that the proposed method for assessment of geometric model parameters is completely based on the actual spatial distribution of rock components and does not require core-based calibration efforts.

Pore-Scale Joint Evaluation of Dielectric Permittivity and Electrical Resistivity for Assessment of Hydrocarbon Saturation Using Numerical Simulations

SPE Journal ◽  
2016 ◽  
Vol 21 (06) ◽  
pp. 1930-1942 ◽  
Author(s):  
Huangye Chen ◽  
Zoya Heidari
Keyword(s):  
Electrical Resistivity ◽  
Spatial Distribution ◽  
Dielectric Permittivity ◽  
Pore Structure ◽  
Maximum Error ◽  
New Method ◽  
Pore Scale ◽  
Resistivity Measurements ◽  
Hydrocarbon Saturation ◽  
Permittivity Measurements

Summary Complex pore geometry and composition, as well as anisotropic behavior and heterogeneity, can affect physical properties of rocks such as electrical resistivity and dielectric permittivity. The aforementioned physical properties are used to estimate in-situ petrophysical properties of the formation such as hydrocarbon saturation. In the application of conventional methods for interpretation of electrical-resistivity (e.g., Archie's equation and the dual-water model) and dielectric-permittivity measurements [e.g., complex refractive index model (CRIM)], the impacts of complex pore structure (e.g., kerogen porosity and intergranular pores), pyrite, and conductive mature kerogen have not been taken into account. These limitations cause significant uncertainty in estimates of water saturation. In this paper, we introduce a new method that combines interpretation of dielectric-permittivity and electrical-resistivity measurements to improve assessment of hydrocarbon saturation. The combined interpretation of dielectric-permittivity and electrical-resistivity measurements enables assimilating spatial distribution of rock components (e.g., pore, kerogen, and pyrite networks) in conventional models. We start with pore-scale numerical simulations of electrical resistivity and dielectric permittivity of fluid-bearing porous media to investigate the structure of pore and matrix constituents in these measurements. The inputs to these simulators are 3D pore-scale images. We then introduce an analytical model that combines resistivity and permittivity measurements to assess water-filled porosity and hydrocarbon saturation. We apply the new method to actual digital sandstones and synthetic digital organic-rich mudrock samples. The relative errors (compared with actual values estimated from image processing) in the estimate of water-filled porosity through our new method are all within the 10% range. In the case of digital sandstone samples, CRIM provided reasonable estimates of water-filled porosity, with only four out of twenty-one estimates beyond 10% relative error, with the maximum error of 30%. However, in the case of synthetic digital organic-rich mudrocks, six out of ten estimates for water-filled porosity were beyond 10% with CRIM, with the maximum error of 40%. Therefore, the improvement was more significant in the case of organic-rich mudrocks with complex pore structure. In the case of synthetic digital organic-rich mudrock samples, our simulation results confirm that not only the pore structure but also spatial distribution and tortuosity of water, kerogen, and pyrite networks affect the measurements of dielectric permittivity and electrical resistivity. Taking into account these parameters through the joint interpretation of dielectric-permittivity and electrical-resistivity measurements significantly improves assessment of hydrocarbon saturation.

Quantitative comparison of processes of oil- and water-based mud-filtrate invasion and corresponding effects on borehole resistivity measurements

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. E57-E73 ◽  
Author(s):  
Jesús M. Salazar ◽  
Carlos Torres-Verdín
Keyword(s):  
Electrical Resistivity ◽  
Flow Rate ◽  
Water Saturation ◽  
Base Case ◽  
Petrophysical Properties ◽  
Resistivity Measurements ◽  
Fluid Saturation ◽  
Mud Cake ◽  
Water Based ◽  
Mud Filtrate

Some laboratory and qualitative studies have documented the influence of water-based mud(WBM)-filtrate invasion on borehole resistivity measurements. Negligible work, however, has been devoted to studying the effects of oil-based mud(OBM)-filtrate invasion on well logs and the corresponding impact on the estimation of petrophysical properties. We quantitatively compare the effects of WBM- and OBM-filtrate invasion on borehole resistivity measurements. We simulate the process of mud-filtrate invasion into a porous and permeable rock formation assuming 1D radial distributions of fluid saturation and fluid properties while other petrophysical properties remain constant. To simulate the process of mud-filtrate invasion, we calculate a time-dependent flow rate of OBM-filtrate invasion by adapting the available formulation of the physics of WBM-filtrate invasion. This approach includes the dynamically coupled effects of mud-cake growth and multiphase filtrate invasion. Simulations are performed with a commercial adaptive-implicit compositional formulation that enables the quantification of effects caused by additional components of mud-filtrate and native fluids. The formation under analysis is 100% water saturated (base case) andis invaded with a single-component OBM. Subsequently, we perform simulations of WBM filtrate invading the same formation assuming that it is hydrocarbon bearing, and compare the results to those obtained in the presence of OBM. At the end of this process, we invoke Archie’s equation to calculate the radial distribution of electrical resistivity from the simulated radial distributions of water saturation and salt concentration and compare the effects of invasion on borehole resistivity measurements acquired in the presence of OBM and WBM. Simulations confirm that the flow rate of OBM-filtrate invasion remains controlled by the initial mud-cake permeability and formation petrophysical properties, specifically capillary pressure and relative permeability. Moreover, WBM causes radial lengths of invasion 15%–40% larger than those associated with OBM as observed on the radial distributions of electrical resistivity. It is found also that, in general, flow rates of WBM-filtrate invasion are higher than those of OBM-filtrate invasion caused by viscosity contrasts between OBM filtrate and native fluids, which slow down the process of invasion. Such a conclusion is validated by the marginal variability of array-induction resistivity measurements observed in simulations of OBM invasion compared with those of WBM invasion.

Effects of Surfactant-Emulsified Oil-Based Mud on Borehole Resistivity Measurements

SPE Journal ◽  
2011 ◽  
Vol 16 (03) ◽  
pp. 608-624 ◽  
Author(s):  
Jesús M. Salazar ◽  
Carlos Torres-Verdín ◽  
Gong Li Wang
Keyword(s):  
Electrical Resistivity ◽  
Spatial Distribution ◽  
Sensitivity Analyses ◽  
High Resistivity ◽  
Reliable Estimate ◽  
Spatial Distributions ◽  
Oil Viscosity ◽  
Resistivity Measurements ◽  
Fluid Saturation ◽  
Mud Filtrate

Summary We quantify the influence of oil-based mud (OBM)-filtrate invasion and formation-fluid properties on the spatial distribution of fluid saturation and electrical resistivity in the near-wellbore region. The objective is to appraise the sensitivity of borehole resistivity measurements to the spatial distribution of fluid saturation resulting from the compositional mixing of OBM and in-situ hydrocarbons. First, we consider a simple two-component formulation for the oil phase (OBM and reservoir oil) wherein the components are first-contact miscible. A second approach consists of adding water and surfactant to a multicomponent OBM invading a formation saturated with multiple hydrocarbon components. Simulations also include presence of irreducible, capillary-bound, and movable water. The dynamic process of OBM invasion causes component concentrations to vary with space and time. In addition, the relative mobility of the oil phase varies during the process of invasion because oil viscosity and oil density are both dependent on component concentrations. Presence of surfactants in the OBM is simulated with a commercial adaptive implicit compositional formulation that models the flow of three-phase multicomponent fluids in porous media. Simulations of the process of OBM invasion yield 2D spatial distributions of water and oil saturation that are transformed into spatial distributions of electrical resistivity. Subsequently, we simulate the corresponding array-induction measurements assuming axial-symmetric variations of electrical resistivity. We perform sensitivity analyses on field measurements acquired in a well that penetrates a clastic formation and that includes different values of density and viscosity for mud filtrate and formation hydrocarbon. These analyses provide evidence of the presence of a high-resistivity region near the borehole wall followed by a low-resistivity annulus close to the noninvaded resistivity region. Such an abnormal resistivity annulus is predominantly caused by high viscosity contrasts between mud filtrate and formation oil. The combined simulation of invasion and array-induction logs in the presence of OBM invasion provides a more reliable estimate of water saturation, which improves the assessment of in-place hydrocarbon reserves.

Simulation of mechanically stirred two-phase liquid-gas flow

1986 ◽  
Vol 51 (5) ◽  
pp. 1001-1015 ◽  
Author(s):  
Ivan Fořt ◽  
Vladimír Rogalewicz ◽  
Miroslav Richter
Keyword(s):  
Spatial Distribution ◽  
Velocity Field ◽  
Gas Flow ◽  
Liquid Velocity ◽  
Model Parameters ◽  
Two Phase ◽  
Mean Velocity ◽  
Rushton Turbine ◽  
Low Viscosity ◽  
Volumetric Gas Flow Rate

The study describes simulation of the motion of bubbles in gas, dispersed by a mechanical impeller in a turbulent low-viscosity liquid flow. The model employs the Monte Carlo method and it is based both on the knowledge of the mean velocity field of mixed liquid (mean motion) and of the spatial distribution of turbulence intensity ( fluctuating motion) in the investigated system - a cylindrical tank with radial baffles at the wall and with a standard (Rushton) turbine impeller in the vessel axis. Motion of the liquid is then superimposed with that of the bubbles in a still environment (ascending motion). The computation of the simulation includes determination of the spatial distribution of the gas holds-up (volumetric concentrations) in the agitated charge as well as of the total gas hold-up system depending on the impeller size and its frequency of revolutions, on the volumetric gas flow rate and the physical properties of gas and liquid. As model parameters, both liquid velocity field and normal gas bubbles distribution characteristics are considered, assuming that the bubbles in the system do not coalesce.

A Theoretical Analysis of CO2 Absorption in Sun Versus Shade Leaves

1978 ◽  
Vol 100 (1) ◽  
pp. 20-24 ◽  
Author(s):  
R. H. Rand
Keyword(s):  
Experimental Data ◽  
Quantitative Estimate ◽  
Geometric Model ◽  
Model Parameters ◽  
Co2 Absorption ◽  
Temperature Model ◽  
Spongy Mesophyll ◽  
One Dimensional ◽  
Mesophyll Tissue ◽  
Shade Leaves

A one-dimensional, steady-state, constant temperature model of diffusion and absorption of CO2 in the intercellular air spaces of a leaf is presented. The model includes two geometrically distinct regions of the leaf interior, corresponding to palisade and spongy mesophyll tissue, respectively. Sun, shade, and intermediate light leaves are modeled by varying the thicknesses of these two regions. Values of the geometric model parameters are obtained by comparing geometric properties of the model with experimental data of other investigators found from dissection of real leaves. The model provides a quantitative estimate of the extent to which the concentration of gaseous CO2 varies locally within the leaf interior.

Combining geophysical methods to estimate the spatial distribution of soils affected by water saturation

2004 ◽  
Vol 336 (6) ◽  
pp. 553-560 ◽  
Author(s):  
Vincent Chaplot ◽  
Christian Walter ◽  
Pierre Curmi ◽  
Alain Hollier-Larousse ◽  
Henri Robain
Keyword(s):  
Spatial Distribution ◽  
Water Saturation ◽  
Geophysical Methods

Machine Learning and Artificial Intelligence Provides Wolfcamp Completion Design Insight

2021 ◽  
Author(s):  
Robert Shelley ◽  
Oladapo Oduba ◽  
Howard Melcher
Keyword(s):  
Machine Learning ◽  
Water Saturation ◽  
Saturation Pressure ◽  
Ann Model ◽  
Permian Basin ◽  
Treatment Rate ◽  
Feed Forward Neural Network ◽  
Well Production ◽  
Reservoir Type ◽  
The Impact

Abstract The subject of this paper is the application of a unique machine learning approach to the evaluation of Wolfcamp B completions. A database consisting of Reservoir, Completion, Frac and Production information from 301 Multi-Fractured Horizontal Wolfcamp B Completions was assembled. These completions were from a 10-County area located in the Texas portion of the Permian Basin. Within this database there is a wide variation in completion design from many operators; lateral lengths ranging from a low of about 4,000 ft to a high of almost 15,000 ft, proppant intensities from 500 to 4,000 lb/ft and frac stage spacing from 59 to 769 ft. Two independent self-organizing data mappings (SOM) were performed; the first on completion and frac stage parameters, the second on reservoir and geology. Characteristics for wells assigned to each SOM bin were determined. These two mappings were then combined into a reservoir type vs completion type matrix. This type of approach is intended to remove systemactic errors in measuement, bias and inconsistencies in the database so that more realistic assessments about well performance can be made. Production for completion and reservoir type combinations were determined. As a final step, a feed forward neural network (ANN) model was developed from the mapped data. This model was used to estimate Wolfcamp B production and economics for completion and frac designs. In the performance of this project, it became apparent that the incorporation of reservoir data was essential to understanding the impact of completion and frac design on multi-fractured horizontal Wolfcamp B well production and economic performance. As we would expect, wells with the most permeability, higher pore pressure, effective porosity and lower water saturation have the greatest potential for hydrocarbon production. The most effective completion types have an optimum combination of proppant intensity, fluid intensity, treatment rate, frac stage spacing and perforation clustering. This paper will be of interest to anyone optimizing hydraulically fractured Wolfcamp B completion design or evaluating Permian Basin prospects. Also, of interest is the impact of reservoir and completion characteristics such as permeability, porosity, water saturation, pressure, offset well production, proppant intensity, fluid intensity, frac stage spacing and lateral length on well production and economics. The methodology used to evaluate the impact of reservoir and completion parameters for this Wolfcamp project is unique and novel. In addition, compared to other methodologies, it is low cost and fast. And though the focus of this paper is on the Wolfcamp B Formation in the Midland Basin, this approach and workflow can be applied to any formation in any Basin, provided sufficient data is available.

scholarly journals The seven sisters DANCe

2018 ◽  
Vol 612 ◽  
pp. A70 ◽  
Author(s):  
J. Olivares ◽  
E. Moraux ◽  
L. M. Sarro ◽  
H. Bouy ◽  
A. Berihuete ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Model Comparison ◽  
Precise Determination ◽  
Model Parameters ◽  
Data Sets ◽  
Adequate Model ◽  
Comparison Results ◽  
Bayesian Evidence ◽  
Mass Segregation

Context. Membership analyses of the DANCe and Tycho + DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11.5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King’s model, although the Generalised King model introduced here has slightly better fitting properties. Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11.5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.

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