New Analytical Method To Calculate Matrix- and Fluid-Corrected Total Porosity in Organic Shale

2015 ◽  
Vol 18 (04) ◽  
pp. 609-623
Author(s):  
Edwin Ortega ◽  
Carlos Torres-Verdín

Summary Estimation of total porosity from neutron and density porosity logs in organic shale (source rock) is challenging because these logs are substantially affected by fluid and matrix-composition effects. Conventional interpretation of neutron and density porosity logs often includes corrections for shale concentration in which the main objective is to improve the calculation of nonshale porosity in hydrocarbon-bearing zones. These corrections are not desirable in unconventional rock formations because shale pores can be hydrocarbon-saturated. Neutron and density porosity readings across shale zones are sometimes averaged by use of the root-mean-square (RMS) method. We introduce a new and simple analytical expression for total porosity that effectively separates both matrix and fluid effects on neutron and density porosity logs. The expression stems from a new nonlinear mixing law for neutron migration length that is coupled with the linear-density mixing law to calculate total porosity and fluid density. The method is applied in two sequential steps: First, separate corrections for only matrix effects are implemented to enhance the neutron-density crossover for qualitative interpretation of fluid type; second, the coupled equation is used to estimate fluid density and actual porosity devoid of matrix and fluid effects. Calculated porosity and fluid density can be used further to calculate water saturation from density logs. One remarkable feature of this method is the ease with which it can be applied to obtain accurate and reliable results. Application of the method only requires knowledge of single-component nuclear properties and mineral volumetric concentrations. One can obtain nuclear properties from a set of charts for multiple fluid types and minerals provided in this paper, whereas one can calculate mineral concentrations on the basis of available triple combo logs or gamma ray spectroscopy logs. Two synthetic and four field examples (two conventional and two shale-gas reservoirs) are used to test the method. First, we describe an application in a conventional siliciclastic sedimentary sequence in which only shale concentration calculated from gamma ray logs is required to improve the estimation of porosity in shaly sections. Second, we document several applications in which gamma ray spectroscopy logs are used together with a reliable hypothesis for clay type to define mineral properties. Results compare well with nuclear-magnetic-resonance (NMR) and core measurements, whereas the new method outperforms the conventional RMS procedure, especially in the cases of gas-bearing, low-porosity organic shale. The new analytical method can be readily implemented on an Excel spreadsheet and requires minimal adjustments for its operation.

Geophysics ◽  
2017 ◽  
Vol 82 (1) ◽  
pp. D13-D30 ◽  
Author(s):  
Edwin Ortega ◽  
Mathilde Luycx ◽  
Carlos Torres-Verdín ◽  
William E. Preeg

Recent advances in logging-while-drilling sigma measurements include three-detector thermal-neutron and gamma-ray decay measurements with different radial sensitivities to assess the presence of invasion. We have developed an inversion-based work flow for the joint interpretation of multidetector neutron, density, and sigma logs to reduce invasion, shoulder-bed, and well-deviation effects in the estimation of porosity, water saturation, and hydrocarbon type, whenever the invasion is shallow. The procedure begins with a correction for matrix and fluid effects on neutron and density-porosity logs to estimate porosity. Multidetector time decays are then used to assess the radial length of the invasion and estimate the virgin-zone sigma while simultaneously reducing shoulder-bed and well-deviation effects. Density and neutron porosity logs are corrected for invasion and shoulder-bed effects using two-detector density and neutron measurements with the output from the time-decay (sigma) inversion. The final step invokes a nuclear solver in which corrected sigma, inverse of migration length, and density in the virgin zone are used to estimate water saturation and fluid type. The fluid type is assessed with a flash calculation and Schlumberger’s Nuclear Parameter calculation code to account for the nuclear properties of different types of hydrocarbon and water as a function of pressure, temperature, and salinity. Results indicate that accounting for invasion effects is necessary when using density and neutron logs for petrophysical interpretation beyond the calculation of total porosity. Synthetic and field examples indicate that the mitigation of invasion effects becomes important in the case of salty mud filtrate invading gas-bearing formations. The advantage of the developed inversion-based interpretation method is its ability to estimate layer-by-layer petrophysical, compositional, and fluid properties that honor multiple nuclear measurements, their tool physics, and their associated borehole geometrical and environmental effects.


2021 ◽  
pp. 4702-4711
Author(s):  
Asmaa Talal Fadel ◽  
Madhat E. Nasser

     Reservoir characterization requires reliable knowledge of certain fundamental properties of the reservoir. These properties can be defined or at least inferred by log measurements, including porosity, resistivity, volume of shale, lithology, water saturation, and permeability of oil or gas. The current research is an estimate of the reservoir characteristics of Mishrif Formation in Amara Oil Field, particularly well AM-1, in south eastern Iraq. Mishrif Formation (Cenomanin-Early Touronin) is considered as the prime reservoir in Amara Oil Field. The Formation is divided into three reservoir units (MA, MB, MC). The unit MB is divided into two secondary units (MB1, MB2) while the unit MC is also divided into two secondary units (MC1, MC2). Using Geoframe software, the available well log images (sonic, density, neutron, gamma ray, spontaneous potential, and resistivity logs) were digitized and updated. Petrophysical properties, such as porosity, saturation of water, saturation of hydrocarbon, etc. were calculated and explained. The total porosity was measured using the density and neutron log, and then corrected to measure the effective porosity by the volume content of clay. Neutron -density cross-plot showed that Mishrif Formation lithology consists predominantly of limestone. The reservoir water resistivity (Rw) values of the Formation were calculated using Pickett-Plot method.   


2021 ◽  
Author(s):  
Saud K. Aldajani ◽  
Saud F. Alotaibi ◽  
Abdulazeez Abdulraheem

Abstract The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.


2020 ◽  
Vol 53 (2F) ◽  
pp. 83-93
Author(s):  
Salam Abdulrahman

The Jaria Pika Gas field is a domal anticlinal structure in the northeast of Iraq NW trending, about 3.6 km long and 1.9 km wide. The 55 m thick gas bearing Jeribe Formation is the main reservoir. This study intends to well log interpretation to determine the petrophysical properties of the Jeribe Formation in the Jaria Pika Gas Field. Total porosity, effect porosity, and secondary porosity have been calculated from neutron, density, and sonic logs. Porosity is fair to good in the Jeribe formation. From RHOB-NPHI and N/M cross plot, the Jeribe Formation is composed mainly of dolomite, limestone with nodules of anhydrite. The Fatha Formation contains considerable amounts of anhydrite layers, so it's represented the cap rocks for the Jeribe Reservoir which is recognized based on the reading of Gamma-ray log, Density log, Neutron log, and Sonic log. The Jaria Pika is considered as gas field as the Jeribe reservoir rocks are gas saturated ones.


2019 ◽  
Author(s):  
Lili Tian ◽  
Feng Zhang ◽  
Quanying Zhang ◽  
Qian Chen ◽  
Xinguang Wang ◽  
...  

2021 ◽  
pp. 136438
Author(s):  
A. Algora ◽  
E. Ganioğlu ◽  
P. Sarriguren ◽  
V. Guadilla ◽  
L.M. Fraile ◽  
...  

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