scholarly journals Plausibility of Hydrocarbon Potential Analytics of Archie’s Model by Cementation Factor Pliability in Shaly Sand Reservoir

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Anthony John Ilozobhie ◽  
Daniel Ikechukwu Egu
Keyword(s):  
Standard Deviation ◽  
Confidence Intervals ◽  
Water Saturation ◽  
Oil Field ◽  
Hydrocarbon Potential ◽  
Detailed Statistical Analysis ◽  
Shaly Sand ◽  
S Model ◽  
Shaly Sandstone ◽  
Cementation Factor

Comprehensive comparative analyses of 18 shaly sandstone zones in four wells of an Oil Field in the Niger Delta were carried using only the Archie Model with the appropriate cementation factor from a range of 1.3 to 2.0 This was done to comprehensively analyze and statistically validate the need for the applicability of m = 1.3. Detailed statistical analysis of water saturation results of lower and upper 95% confidence intervals for the standard deviations gave the least range of 0.00415 to 0.00724 (m=1.3), 0.00660 to 0.1151 (m=1.65) and maximum of 0.00996 to 0.01747 (m=2.0). This was however validated by the bias results of the standard deviation with -0.00025 for m=1.3, -0.00040 for m=1.65 and -0.00060 for m=2. Hydrocarbon saturation results of lower and upper 95% confidence intervals for the standard also gave the least values of 0.00427 to 0.00740 (m=1.3), 0.00680 to 0.01171 (m=1.65) and 0.01031 to 0.01773 (m=2.0). The bias results of the standard deviation gave the least for m=1.3 as -0.0002, -0.00032 for m=1.65 and -0.00048 for m=2.0. Hydrocarbon movability index results of lower and upper 95% confidence intervals for standard deviation gave the least range for m=1.3 of 0.00521 to 0.00934, 0.00793 to 0.01415 for m=1.65 and 0.01155 to 0.02049 for m=2.0. The bias results of the standard deviation gave also the least for m=1.3 as -0.00031, -0.00047 for m=1.65 and -0.00068 for m=2. The study reveals that the Archie Model predictions was improved with cementation factor of 1.3 and has favourable petrophysical parameters indicating higher hydrocarbon potential than the Simandoux  and when m=1.65 and 2.0. This model is a valuable tool in a shaly sand environment after thorough validation using the pickett plot.

scholarly journals Improved water saturation estimation in shaly sandstone through variable cementation factor

2021 ◽  
Author(s):  
Wan Zairani Wan Bakar ◽  
Ismail Mohd Saaid ◽  
Mohd Riduan Ahmad ◽  
Zulhelmi Amir ◽  
Nur Shuhadah Japperi ◽  
...  
Keyword(s):  
Water Saturation ◽  
Percentage Error ◽  
Saturation Model ◽  
Formation Factor ◽  
Archie’S Law ◽  
Straight Line ◽  
The Difference ◽  
Shaly Sandstone ◽  
Cementation Factor ◽  
Surface Conduction

AbstractEstimation of water saturation, Sw, in shaly sandstone is an intricate process. The surface conduction of clay minerals adds up to the electrolyte conduction in the pore spaces, thus generating high formation conductivity that overshadows the hydrocarbon effect. In each resistivity-based water saturation model, the key parameter is formation factor, F, which is typically derived from Archie’s Law. Referring to a log–log plot between formation factor and porosity, cementation factor reflects the slope of the straight line abiding Archie’s Law. In the case of shaly sandstone, derivation based on Archie’s Law in combination with Waxman–Smits equation leads to higher cementation factor, m*. In the shaly parts of the reservoir, high m* is counterbalanced by clay conductivity. Nonetheless, high m* used in clean parts increases Sw estimation. In this study, the variable cementation factor equation is introduced into the standard correlation of Sw versus Resistivity Index, RI, to develop a water saturation model with shaly sandstone parameters. Data retrieved from two fields that yielded mean arctangent absolute percentage error (MAAPE) were analysed to determine the difference between calculated and measured data within the 0.01–0.15 range for variable cementation factor method. The conventional method yielded maximum MAAPE at 0.46.

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.

Delineation of hydrocarbon potential zones in Masila oil field, Yemen

2018 ◽  
Vol 27 (2) ◽  
pp. 121-135 ◽  
Author(s):  
Mohammed Sultan Alshayef ◽  
Akram Javed ◽  
Arafat Mohammed Bin Mohammed
Keyword(s):  
Oil Field ◽  
Hydrocarbon Potential

scholarly journals HYDROCARBON POTENTIALITY OF FACHA RESERVOIR IN ASWAD OIL FIELD, SIRT BASIN, LIBYA

2020 ◽  
Vol 3 (1) ◽  
pp. 62-77
Author(s):  
A. A. Kushlaf ◽  
A. E. El Mezweghy
Keyword(s):  
Water Saturation ◽  
Oil Field ◽  
Reservoir Rock ◽  
Physical Parameters ◽  
Structural Framework ◽  
Average Porosity ◽  
Average Water ◽  
Exploratory Wells ◽  
Hydrocarbon Potentiality ◽  
Sirt Basin

This paper is to study the structural framework, stratigraphy, and the petro-physical characteristics of Facha reservoir of Gir Formation in Aswad oil field, which is located in Block NC74B at the Zella Trough, south-west of Sirt basin, Libya. The data used have been got from well-logging records of nine exploratory wells distributed in Aswad oil field. These data have been analyzed and interpreted through using analytical cross-plots in order to calculate the petro-physical parameters. The results revealed that the lithological facies consists mainly of dolomite. Moreover, they revealed that the lateral distribution of the petro-physical parameters of Facha reservoir indicates that average porosity is 10-23%, average water saturation is 52- 93%, and net pay is of 62.44 ft. This shows that Facha member is a good reservoir rock. The variations in values between wells have been affected by the trend of faults; this indicates that the area is structurally controlled.

scholarly journals Petrophysical Properties and Digenetic Evolution of Shuaiba Formation in Nasiriyah Oil Field, Southern Iraq

2021 ◽  
pp. 4810-4818
Author(s):  
Marwah H. Khudhair
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Middle Part ◽  
Effective Porosity ◽  
Oil Field ◽  
Neutron Density ◽  
Petrophysical Properties ◽  
Secondary Porosity ◽  
Primary Porosity ◽  
Gamma Ray Log

     Shuaiba Formation is a carbonate succession deposited within Aptian Sequences. This research deals with the petrophysical and reservoir characterizations characteristics of the interval of interest in five wells of the Nasiriyah oil field. The petrophysical properties were determined by using different types of well logs, such as electric logs (LLS, LLD, MFSL), porosity logs (neutron, density, sonic), as well as gamma ray log. The studied sequence was mostly affected by dolomitization, which changed the lithology of the formation to dolostone and enhanced the secondary porosity that replaced the primary porosity. Depending on gamma ray log response and the shale volume, the formation is classified into three zones. These zones are A, B, and C, each can be split into three rock intervals in respect to the bulk porosity measurements. The resulted porosity intervals are: (I) High to medium effective porosity, (II) High to medium inactive porosity, and (III) Low or non-porosity intervals. In relevance to porosity, resistivity, and water saturation points of view, there are two main reservoir horizon intervals within Shuaiba Formation. Both horizons appear in the middle part of the formation, being located within the wells Ns-1, 2, and 3. These intervals are attributed to high to medium effective porosity, low shale content, and high values of the deep resistivity logs. The second horizon appears clearly in Ns-2 well only.

scholarly journals Petrophysical Properties of Nahr Umar Formation in Nasiriya Oil Field

2020 ◽  
Vol 21 (3) ◽  
pp. 9-18
Author(s):  
Ahmed Abdulwahhab Suhail ◽  
Mohammed H. Hafiz ◽  
Fadhil S. Kadhim
Keyword(s):  
Gamma Ray ◽  
Water Saturation ◽  
Oil Field ◽  
Well Logs ◽  
High Porosity ◽  
Reservoir Properties ◽  
Petrophysical Parameters ◽  
Resistivity Logs ◽  
Lithology Prediction ◽  
Porosity And Permeability

   Petrophysical characterization is the most important stage in reservoir management. The main purpose of this study is to evaluate reservoir properties and lithological identification of Nahr Umar Formation in Nasiriya oil field. The available well logs are (sonic, density, neutron, gamma-ray, SP, and resistivity logs). The petrophysical parameters such as the volume of clay, porosity, permeability, water saturation, were computed and interpreted using IP4.4 software. The lithology prediction of Nahr Umar formation was carried out by sonic -density cross plot technique. Nahr Umar Formation was divided into five units based on well logs interpretation and petrophysical Analysis: Nu-1 to Nu-5. The formation lithology is mainly composed of sandstone interlaminated with shale according to the interpretation of density, sonic, and gamma-ray logs. Interpretation of formation lithology and petrophysical parameters shows that Nu-1 is characterized by low shale content with high porosity and low water saturation whereas Nu-2 and Nu-4 consist mainly of high laminated shale with low porosity and permeability. Nu-3 is high porosity and water saturation and Nu-5 consists mainly of limestone layer that represents the water zone.

15. Quantitative Analysis: Description and Inference

2020 ◽  
pp. 393-421
Author(s):  
Sandra Halperin ◽  
Oliver Heath
Keyword(s):  
Quantitative Analysis ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quantitative Research ◽  
Building Blocks ◽  
Descriptive Statistics ◽  
Central Tendency ◽  
Margin Of Error ◽  
Univariate Statistics ◽  
The Mean

This chapter deals with quantitative analysis, and especially description and inference. It introduces the reader to the principles of quantitative research and offers a step-by-step guide on how to use and interpret a range of commonly used techniques. The first part of the chapter considers the building blocks of quantitative analysis, with particular emphasis on different ways of summarizing data, both graphically and with tables, and ways of describing the distribution of one variable using univariate statistics. Two important measures are discussed: the mean and the standard deviation. After elaborating on descriptive statistics, the chapter explores inferential statistics and explains how to make generalizations. It also presents the concept of confidence intervals, more commonly known as the margin of error, and measures of central tendency.

scholarly journals Robust Confidence Intervals for PM2.5 Concentration Measurements in the Ecuadorian Park La Carolina

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 654 ◽  
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Rasa Zalakeviciute ◽  
Angela Maria Diaz-Marquez
Keyword(s):  
Air Pollution ◽  
Standard Deviation ◽  
Confidence Intervals ◽  
Quality Index ◽  
Urban Park ◽  
Absolute Deviation ◽  
The Mean ◽  
Concentration Measurements ◽  
Pm2.5 Concentration

In this article, robust confidence intervals for PM2.5 (particles with size less than or equal to 2.5   μ m ) concentration measurements performed in La Carolina Park, Quito, Ecuador, have been built. Different techniques have been applied for the construction of the confidence intervals, and routes around the park and through the middle of it have been used to build the confidence intervals and classify this urban park in accordance with categories established by the Quito air quality index. These intervals have been based on the following estimators: the mean and standard deviation, median and median absolute deviation, median and semi interquartile range, a -trimmed mean and Winsorized standard error of order a , location and scale estimators based on the Andrew’s wave, biweight location and scale estimators, and estimators based on the bootstrap- t method. The results of the classification of the park and its surrounding streets showed that, in terms of air pollution by PM2.5, the park is not at caution levels. The results of the classification of the routes that were followed through the park and its surrounding streets showed that, in terms of air pollution by PM2.5, these routes are at either desirable, acceptable or caution levels. Therefore, this urban park is actually removing or attenuating unwanted PM2.5 concentration measurements.

scholarly journals A New Model to Calculate Oil-Water Relative Permeability of Shaly Sandstone

Geofluids ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Huiyuan Bian ◽  
Kewen Li ◽  
Binchi Hou ◽  
Xiaorong Luo
Keyword(s):  
Relative Permeability ◽  
Oil Field ◽  
Permeability Model ◽  
Field Development ◽  
Oil Field Development ◽  
Oil Water ◽  
Sandstone Reservoir ◽  
Sandstone Reservoirs ◽  
Resistivity Logging ◽  
Shaly Sandstone

Oil-water relative permeability curves are the basis of oil field development. In recent years, the calculation of oil-water relative permeability in sandstone reservoirs by resistivity logging data has received much attention from researchers. This article first analyzed the existing mathematical models of the relationship between relative permeability and resistivity and found that most of them are based on Archie formula, which assumes the reservoir is clean sandstone. However, in view of the fact that sandstone reservoir is commonly mixed with shale contents, this research, based on the dual water conductivity model, Poiseuille’s equation, Darcy’s law, and capillary bundle model, derived a mathematical model (DW relative permeability model) for shaly sandstone reservoir, which calculates the oil-water relative permeability with resistivity. To test and verify the DW relative permeability model, we designed and assembled a multifunctional core displacement apparatus. The experiment of core oil-water relative permeability and resistivity was designed to prove the effectiveness of the DW relative permeability model in shaly sandstone reservoirs. The results show that the modified Li model can well express the transformational relation between resistivity and relative permeability in sandstone reservoir with low clay content. Compared with the modified Li model and the Pairoys model, the DW relative permeability model is more helpful to collect better results of relative permeability in shaly sand. These findings will play a significant role in the calculation of oil-water relative permeability in reservoirs based on resistivity logging data and will provide important data and theory support to the shaly sandstone reservoir characterized oil field development.

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