Shale Indicator Derived from Multivariate Statistical Analysis of Well Logs (original) (raw)
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Nafta-Gaz, 2016
The paper presents the use of statistical factor analysis for the reduction of the number of physicochemical measurement variables, based on the example of well data from shale formations. The main objective of the conducted analyses was the reduction of the number of measurement variables in a manner which would enable the preservation of the possibly high amount of information about the variability of the original data. The presented procedure consists of two main stages: 1) the elimination of correlated variables, 2) the actual factor analysis. The method turned out to be effective and it can constitute a basis for further analyses, e.g. an agglomeration analysis.
UMYU Scientifica, Vol. 1 NO. 1, September 2022, Pp 20 – 29, 2022
Accurate shale volume estimation is an important approach in reservoirs characterization as it forms the basis upon which evaluators can ascertain the hydrocarbon content of the reservoirs. The porosity, gamma ray, neutron-density and deep induction logs data were used to arrive at suitable shale volume estimates of the field studied. Analysis of well logs data was done using the TECHLOG Exploration software. Delineation of reservoirs was carried out with OpendTect software. The Microsoft excel spreadsheet was utilized to accurately estimate other suitable petrophysical parameters such as the permeability, water saturation, hydrocarbon saturation and the porosity. Three different non-linear shale volume models, the Larionov, the Steiber and the Clavier models were used to determine the reservoirs’ shale content across three wells of Yewa reservoirs characterized by varying thicknesses. Variation in the depths down hole for each of the methods revealed that shale volume estimates with the Larionov model was determined across thickness 142.646 m with top and bottom depths of 1946.605 m and 2089.252 m respectively in well Y1, thicknes 90.678 m with top and bottom depths of 2164.690 m and 2255.368 m respectively in well Y2 and thickness 107.290 m with top and bottom depths of 2303.374 m and 2410.663 m respectively in well Y3. The estimates with Steiber model were respectively determined across thicknesses 85.649 m, 95.098 m and 121.371 m for Y1, Y2 and Y3 reservoirs, and top and bottom depths of 1947.571 m and 2033.219 m in well Y1, 2041.754 m and 2136.851 m in well Y2 and 2144.979 m and 2266.442 m in well Y3 and the one with Clavier model were respectively determined across thicknesses 146.456 m, 147.752 m and 94.869 m for Y1, Y2 and Y3 reservoirs and top and bottom depths of 1760.601 m and 1907.057 m in well Y1, 1920.312 m and 2068.068 m in well Y2 and 2078.812 m and 2173.681 m in well Y3. The lowest shale volume average estimate was recorded from the Larionov model. Nevertheless, one cannot conclude that the Larionov model is the most reliable as values obtained may be because of instability in the sensitivities of utilized well logs and the complexities in the properties of wells down hole. A further investigation of the sensitivities of the well logs and the down hole properties of the wells showed that the Larionov method gives reasonable, consistent, and repetitive intervals when compared with the Steiber and the Clavier models. The Larionov model is hereby recommended for use in the study area.
Hydrogeology Journal, 2013
The calculation of groundwater reserves in shaly sand aquifers requires a reliable estimation of effective porosity and permeability. The amount of shaliness as a related quantity can be extracted from geophysical well log analysis. The conventionally used linear model connecting natural gamma-ray index to shale content often gives a rough estimate in shallow boreholes. To get a better result a non-linear model is suggested, which is derived from the factor analysis of well-logging data. An earlier study of hydrocarbon wells revealed an empirical relationship between the factor scores and shale volume independent of the well site. Borehole logs measured from three groundwater wells drilled in Hungary are analyzed to estimate the logs of factor variables, which are then correlated with shale volumes given from the method of Larionov. Shale volume logs estimated by the statistical procedure are in sufficiently close agreement with those derived from the Larionov's formula that confirms the validity of the nonlinear approximation. The statistical results are accordance with laboratory measurements made on core samples. However, whereas the conventional borehole geophysical methods normally use a single well log as an input, factor analysis processes all available logs to provide groundwater exploration with highly reliable estimation results.
Estimation of shale volume using a combination of the three porosity logs
Journal of Petroleum Science and Engineering, 2003
An equation was developed for evaluating the volume of shale using standard porosity logs such as neutron, density and acoustic logs. The equation is written in terms of several parameters that are readily available from well-log measurements. This equation, which takes into consideration the effect of matrix, fluid and shale parameters, applies reasonably well for many shaly formations independent of the distribution of shales. The results demonstrate the applicability of the equation to well-log interpretation as a procedure for computing shale volume in shaly sand sedimentary sections. Three key advantages of the proposed equation are: (1) it incorporates several parameters that directly or indirectly affect the determination of shale in one equation, (2) it integrates the three porosity tools for a more accurate determination, and (3) it works well in hydrocarbon-bearing formations and where radioactive material other than shale is present. Successful application of the equation to shaly sand reservoirs is illustrated by analyses of samples from the Gulf of Suez.
Estimating Volume of Shale in a Clastic Niger Delta Reservoir from Well Logs: A Comparative Study
International Journal of Geosciences, 2021
The volume of shale (V sh) is a critical parameter in petrophysical analysis that enables the accurate estimation of other petrophysical parameters like effective porosity, saturation and Net-to-Gross. This is an important step in characterization of reservoirs as well as valuation of hydrocarbon potentials. GR (Gamma Ray), Neutron and Density as well as Potassium, Uranium and Thorium logs were adopted to estimate and analyze V sh for sand 4 reservoir interval across five wells using the empirical (GR-linear and non-linear) and Neutron-Density methods. Results show that V sh estimated by the different methods varied from 0.24-0.39 for the GR linear method (highest), 0.12-0.24 for the Larionov method (intermediate), and 0.04-0.28 for the Neutron-Density method (lowest). Although the Neutron-Density method gives the lowest values of volume of shale, this does not translate to the most accurate and reliable results. This may be attributed to the non-singularity in measurements and varying sensitivities of the well logs used in this method as well as the complexities of the wellbore condition. The GR non-linear (Larionov) method provides consistent and comparable volume of shale estimations with the neutron-density method than the linear GR method and consequently, the non-linear GR method is recommended for estimation of V sh in the studied field.
2015
The article presents the methodological aspects of hydrocarbon resources calculation accumulated in shale formations using two variants of the volumetric method based on different data sets. The first method constitutes an extension of the classic volumetric method taking into account adsorbed gas presence on kerogen surface. This method can be applied to formations saturated with oil, condensate, as well as dry gas. The second proposed method can be used for resources calculations in oil-saturated reservoirs only. It involves the use of geochemical data (Rock Eval pyrolysis data), results of PVT measurements of reservoir fluids and Langmuir isotherm. The possibility of using different methodological approaches allows to carry out calculations in different conditions of data availability. Both methods, used for test calculations of hydrocarbon resources in oil type shales, give surprisingly consistent results.
SPE reservoir evaluation & engineering, 2018
Recent progress has increased our understanding of key controls on the productivity of shale reservoirs. The quantitative relations between regional Eagle Ford Shale production trends and geologic parameters were investigated to clarify which geologic parameters exercise dominant control on well-production rates. Previously, qualitative correlations for the Eagle Ford Shale were demonstrated among depth, thickness, total organic carbon (TOC), distribution of limestone beds, and average bed thickness with regional production. Eagle Ford production wells are horizontal, but it was necessary to use vertical wells that penetrated the Eagle Ford to map reservoir properties. No wells in the database had both production and geological parameters, and thus geological parameters could not be directly related to individual-well production. Therefore, spatial-interpolation methods derived from the Kriging and Bayesian methods with Markov-chain Monte Carlo (MCMC) sampling algorithms were used to integrate data sets and predict geological properties at production-well locations. The spatial Gaussianprocess-regression modeling was conducted to investigate the primary controls on production. Results suggest that the 6-month cumulative production from the Eagle Ford Shale, in barrels of oil equivalent (BOE), increases consistently with depth, with Eagle Ford thickness (up to 180-ft thickness), and with TOC (up to 7%). Also, when the number of limestone beds exceeds 12, production increases with the number of limestone beds. The corresponding significance code indicates that the parameters most significant to production are TOC and depth (which relates to pressure and thermal maturation). Concepts and models developed in this study may assist operators in making critical Eagle Ford Shale development decisions and should be transferable to other shale plays. The Eagle Ford Shale dips southeast from the outcrop; depth exceeds 13,000 ft at the Sligo Shelf Margin (Fig. 1) (Hentz and Ruppel 2010; Tian et al. 2012). The Eagle Ford Shale is divided into three units. The Lower Eagle Ford is present throughout the study area; it is more than 275 ft thick in the Maverick Basin depocenter and thins to less than 50 ft on the northeast (Fig. 2) (Tian et al. 2012). The TOC of the lower Upper Eagle Ford Shale is greatest (7%) in Zavala and Frio Counties (Tian et al. 2012). A strike-elongate trend of high TOC, high average gamma ray responses, and low bulk density extends from Maverick County northeast through Guadalupe County, parallel to and updip of the Sligo and Stuart City Shelf Margins (Fig. 3) (Tian et al. 2013). The numbers of both limestone and organic-rich marl (ORM) beds, which together comprise the Eagle Ford Shale, increase from fewer than two near the outcrop in the northwest to more than 20 on the southeast at the Sligo Shelf Margin (Fig. 4). Average thickness of limestone and ORM beds in the Lower Eagle Ford Shale is low (<5 ft) in the La Salle/DeWitt trend, coincident with the most-productive gas and oil regions, respectively (Fig. 5) (Tian et al. 2014). After characterizing the key geological parameters that may affect Eagle Ford production, the questions remained: "Is there a quantitative relationship between production and the previously discussed parameters?" And, if so, "Which geological parameter has the dominant control on production?" The difficulty of relating geological parameters to production occurs because no wells in the database have both production and geological data. The wells used to calculate geological parameters are vertical wells that were drilled for reservoirs below the Eagle Ford Shale. However, the production wells are horizontal wells. Therefore, production data cannot be directly related to geological data at individual-well locations. Methods As mentioned, one of the challenges of correlating the effect of geological parameters with production relates to the misalignment between horizontal producers and geological data from nearby vertical wells. Wells with complete measurements in our database are quite limited. The database has both vertical deeper-well logs that penetrate Eagle Ford Shale but do not have Eagle Ford production, and Eagle Ford horizontal wells that do not penetrate the full formation but have production data. Therefore, interpolation of reservoir properties from vertical-well locations to horizontal wells is required. An estimated spatial-covariance function is crucial to achieving an accurate interpolation. Identifying the distribution pattern for each variable is the key to selecting the correct method to fit the covariance function. The following work flow was designed to characterize the distributional differences among various data sets. Coefficients
An Integrated Approach to Volume of Shale Analysis: Niger Delta Example, Offrire Field
World Applied Sciences Journal, 2009
An integrated approach for the estimation of volume of shale from a suite of logs comprising the gamma ray, neutron-density combination, resistivity, combination of different methods (total) was carried in an Old Niger Delta Field called Orire with a view to ascertaining the reservoir quality and mapping reservoir bodies for further petrophysical analysis. The volume of shale (Vsh) calculation based on naturally occurring gamma ray frequently overestimates shale volume when encounters radioactive sand as sand appears shaly. In this situation, a Vsh calculation from neutron-density data yields a more accurate shale volume but in the presence of gas or light hydrocarbon, this approach is less accurate. The deficiency in this method is now addressed by Vsh calculation from the resistivity data. To avoid overestimation or underestimation of shale volume from any of the methods, the three methods were integrated to obtain Vsh total which finds the minimum value of all the methods. This approach distinguishes properly permeable bed (sand) from non-permeable bed (shale). Also from this method, the depositional environment was easily inferred.
CT&F - Ciencia, Tecnología y Futuro, 2019
The office U.S. Energy Information Administration (EIA) has suggested significant volumes of hydrocarbon resources in unconventional Shale type reservoirs, which happens to be very interesting nowadays. The complexity of these reservoirs, along with the high level of risk during the exploration stage, and the lack of laboratory data, are challenging for an adequate estimation of hydrocarbon volumes in shale reservoirs. An innovative methodology to estimate prospective resources on a Shale reservoir is proposed in this paper, based on petrophysical and geochemical data from well logs, such as porosity, hydrocarbon saturation, TOC (total organic content), gas content, thermal rock maturity, clay fraction, thickness, rock density, etc, all of them using Monte Carlo simulation. Further, this paper proposes a new way of interpreting petrophysical data to obtain a clearer view of reservoir characterization, especially Brittleness, which is of great relevance to define the ...