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.

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.

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.

scholarly journals A New Method To Estimate Resistivity Distribution Of Shaly Sand Reservoirs Using New Seismic Attributes

2021 ◽  
Vol 71 ◽  
pp. 149-157
Author(s):  
Nur Farhana Salleh ◽  
◽  
Maman Hermana ◽  
Deva Prasad Ghosh
Keyword(s):  
Water Saturation ◽  
Seismic Attributes ◽  
Effective Porosity ◽  
Seismic Attenuation ◽  
Hydrocarbon Exploration ◽  
Fluid Distribution ◽  
Similar Response ◽  
Petrophysical Properties ◽  
Resistivity Model ◽  
Shaly Sand

A subsurface resistivity model is important in hydrocarbon exploration primarily in the controlled-source electromagnetic (CSEM) method. CSEM forward modelling workflow uses resistivity model as the main input in feasibility studies and inversion process. The task of building a shaly sand resistivity model becomes more complex than clean sand due to the presence of a shale matrix. In this paper, a new approach is introduced to model a robust resistivity property of shaly sand reservoirs. A volume of seismic data and three wells located in the K-field of offshore Sarawak is provided for this study. Two new seismic attributes derived from seismic attenuation property called SQp and SQs are used as main inputs to predict the volume of shale, effective porosity, and water saturation before resistivity estimation. SQp attribute has a similar response to gamma-ray indicating the lithological variation and SQs attribute is identical to resistivity as an indicator to reservoir fluids. The petrophysical predictions are performed by solving the mathematical step-wise regression between the seismic multi-attributes and predicted petrophysical properties at the well locations. Subsequently, resistivity values are estimated using the Poupon-Leveaux (Indonesia) equation, an improvised model from Archie’s to derive the mathematical relationship of shaly sand’s resistivity to the volume and resistivity of clay matrix in shaly sand reservoirs. The resistivity modeled from the predicted petrophysical properties distributed consistently with sand distribution delineated from SQp attribute mainly in southeast, northeast, and west regions. The gas distribution of the net sand modeled by 5% and 90% of gas saturation scenarios also changed correspondingly to SQs attribute anomaly indicating the consistent fluid distribution between the modeled resistivity and SQs attribute.

New Insights in the Evaluation of Reserves of Selected wells of the Pletmos Basin Offshore South Africa

2016 ◽  
Author(s):  
Samir Elamri ◽  
Mimonitu Opuwari
Keyword(s):  
South Africa ◽  
Water Saturation ◽  
Base Case ◽  
Reservoir Properties ◽  
Vertical Heterogeneity ◽  
Structural Configurations ◽  
Shaly Sand ◽  
Core Description ◽  
Hydrocarbon Reserves ◽  
High Uncertainty

ABSTRACT The area evaluated has similar structural styles and settings as the producing neighboring fields of F-A and E-M in the adjacent Bredasdorp basin Offshore South Africa. The main objective of this study is to create a 3-D-static model and estimate hydrocarbon reserves. Based on log signatures, petrophysical properties and structural configurations, the reservoirs were divided vertically into three reservoir units in order to be properly modelled in 3-D space. The thicknesses of the layers were determined based on the vertical heterogeneity in the reservoir properties. Facies interpretation was performed based on log signatures, core description and previous geological studies. The volume of clay and porosity was used to classify facies into five units of sand, shaly sand, silt, and clay. From petrophysical interpretation, a synthetic permeability log was generated in the wells which ties closely with core data. The J-function water saturation model was adopted because it produced better results in the clean sandstone sections of the reservoirs. A high uncertainty in the basement formation was observed due to very few wells drilled in the area and fault impact and thus resulted in evaluation of uncertainty of each zone separately. The uncertainty workflow was run using 100 trials and the base case P50 estimated 277 Bcf of Gas from the 1At1.

Joint inversion of marine seismic AVA and CSEM data using statistical rock-physics models and Markov random fields

Geophysics ◽  
2012 ◽  
Vol 77 (1) ◽  
pp. R65-R80 ◽  
Author(s):  
Jinsong Chen ◽  
G. Michael Hoversten
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Water Saturation ◽  
Joint Inversion ◽  
Rock Physics ◽  
Model Parameters ◽  
Significant Information ◽  
Markov Random ◽  
Borehole Logs ◽  
Statistical Rock Physics

Joint inversion of seismic AVA and CSEM data requires rock-physics relationships to link seismic attributes to electric properties. Ideally, we can connect them through reservoir parameters (e.g., porosity and water saturation) by developing physical-based models, such as Gassmann’s equations and Archie’s law, using nearby borehole logs. This could be difficult in the exploration stage because information available is typically insufficient for choosing suitable rock-physics models and for subsequently obtaining reliable estimates of the associated parameters. The use of improper rock-physics models and the inaccuracy of the estimates of model parameters may cause misleading inversion results. Conversely, it is easy to derive statistical relationships among seismic and electric attributes and reservoir parameters from distant borehole logs. In this study, we developed a Bayesian model to jointly invert seismic AVA and CSEM data for reservoir parameters using statistical rock-physics models; the spatial dependence of geophysical and reservoir parameters were carried out by lithotypes through Markov random fields. We applied the developed model to a synthetic case that simulates a CO2 monitoring application. We derived statistical rock-physics relations from borehole logs at one location and estimated seismic P- and S-wave velocity ratio, acoustic impedance, density, electric resistivity, lithotypes, porosity, and water saturation at three different locations by conditioning to seismic AVA and CSEM data. Comparison of the inversion results with their corresponding true values showed that the correlation-based statistical rock-physics models provide significant information for improving the joint inversion results.

Formation-Resistivity Theory: How Archie Equations, Shaly-Reservoir Models, Conductive Rock-Matrix Model, and Dual-Triple-Porosity Models Are Related

2014 ◽  
Vol 17 (02) ◽  
pp. 141-151 ◽  
Author(s):  
Philip C. Iheanacho
Keyword(s):  
Water Saturation ◽  
Control Volume ◽  
Single Parameter ◽  
Rock Matrix ◽  
Heterogeneous Reservoirs ◽  
Resistivity Logs ◽  
Resistivity Model ◽  
Formation Resistivity ◽  
Shaly Sand ◽  
New Equation

Summary The estimation of hydrocarbon pore volume (HCPV) from resistivity logs can be quite troublesome in some complex heterogeneous reservoirs. Most water-saturation/formation-resistivity models that work well for some reservoirs give unreliable results for others. No single model works for all types of reservoir scenarios. This paper presents the theory of formation resistivity in porous media. The paper develops the theory from the parallel-resistivity model and then extends it for the series-resistivity model. When applied for clean sand, the theory derives Archie equations from the first principle. The derivations show that both porosity exponent and saturation exponent are of the same origin and should have the same name. A better name for both parameters should be the tortuosity exponent of a component with respect to its fraction in a control volume. It is also advantageous to treat as a single parameter rather than two separate parameters. In addition, this theory derives new shaly-sand models for estimating HCPV. These new shaly-sand models can be used for different types of shale distribution by adjusting the value of a single parameter in the models. The formation-resistivity theory is also used to derive formation-resistivity models for conductive rock-matrix reservoirs and dual-triple-porosity reservoirs. A new equation for calculating the composite-porosity exponent is also developed. Field data are used to validate this work. The theory, when applied for each scenario, derives formation-resistivity models for estimating the reliable HCPV of different reservoir scenarios and types. Moreover, the strength of this theory is its ability to generate models that closely resemble models that have proved to work well for the reservoir cases for which they were developed. Although this work does not test the theory for the cases of tight-sand, shale-gas, and other unconventional reservoirs because of the unavailability of such data, the author is of the opinion that the theory can easily be extended for such reservoirs if the necessary data are available.

Rock physics characterization of chalk by combining acoustic and electromagnetic properties

Geophysics ◽  
2021 ◽  
pp. 1-65
Author(s):  
Hemin Yuan ◽  
Majken C. Looms ◽  
Lars Nielsen
Keyword(s):  
Reservoir Characterization ◽  
Water Saturation ◽  
Rock Physics ◽  
Cost Effective ◽  
Geological Mapping ◽  
P Wave ◽  
Electromagnetic Properties ◽  
Model Parameters ◽  
Near Surface

The characterization of shallow subsurface formations is essential for geological mapping and interpretation, reservoir characterization, and prospecting related to mining/quarrying. To analyze elastic and electromagnetic properties, we characterize near-surface chalk formations deposited on a shallow seabed during the Late Cretaceous–Early Paleogene (Maastrichtian-Danian). Electromagnetic and elastic properties, both of which are related to mineralogy, porosity, and water saturation, are combined to characterize the physical properties of chalk formations. We also perform rock physics modeling of elastic velocities and permittivity and analyze their relationships. We then use measured ground penetrating radar and P-wave velocity field data to determine the key model parameters, which are essential for the validity of the models and can be used to evaluate the consolidation degree of the rocks. Based on the models, a scheme is developed to estimate the porosity and water saturation by combining the two rock physics templates. The predictions are consistent with previous findings. Our templates facilitate fast mapping of near-surface porosity and saturation distributions and represent an efficient and cost-effective method for near-surface hydrological, environmental, and petrophysical studies. In the current formulation, the method is only applicable to rock type (chalk) comprising a single mineral (pure calcite). It is possible to tailor the formulation to include more than one mineral; however, this will increase the uncertainty of the results.

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.

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.

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