scholarly journals Quantifying occurrence of deformation bands in sandstone as a function of structural and petrophysical factors and their impact on reservoir quality: an example from outcrop analog of Productive Series (Pliocene), South Caspian Basin

2022 ◽  
Author(s):  
Shahriyar Alkhasli ◽  
Gasham Zeynalov ◽  
Aydin Shahtakhtinskiy
Keyword(s):  
Gamma Ray ◽  
Field Measurements ◽  
Integrated Approach ◽  
Rock Properties ◽  
Caspian Basin ◽  
Positive Trend ◽  
Deformation Bands ◽  
South Caspian Basin ◽  
Reservoir Properties ◽  
Natural Gamma

AbstractDeformation bands (DB) are known to influence porosity and permeability in sandstones. This study aims to predict the occurrence of DB and to quantify their impact on reservoir properties based on field measurements in the steeply dipping limb of a kilometer-scale fold in Yasamal Valley, western South Caspian Basin. An integrated approach of characterizing bands and their effect on reservoir properties included measurements of natural gamma radioactivity and permeability using portable tools, along with bed dip and the count of DB across distinct facies. A set of core analyses was performed on outcrop plugs with and without bands to estimate the alteration of rock properties at the pore scale. Interpretation of outcrop gamma-ray data indicates the absence of bands in Balakhany sandstones containing shale volume greater than 18% for unconsolidated and 32% for calcite-rich facies. A high amount of calcite cement appears to increase the number of DB. A poor, positive trend between bed dip and DB concentration was identified. We show that net to gross, defined as the thickness fraction of sandstone bound by mudstones, is among the parameters controlling the occurrence of bands. Samples containing a single DB show a 33% and 3% decrease in permeability and porosity, respectively, relative to the host rock. We reveal a new set of lithological and petrophysical factors influencing DB occurrence. This study offers a direct tool that can be applied in subsurface reservoir analogs to predict the occurrence and concentration of DB and estimate their influence on rock properties.

Integrated Approach to Building a Basin Model for the Red Series, South Caspian Basin

2006 ◽  
Author(s):  
Othman Ali Mahmud ◽  
Peter Abolins ◽  
Wim Kampscuur ◽  
Liau Min Hoe and Carlo Sanders
Keyword(s):  
Integrated Approach ◽  
Caspian Basin ◽  
South Caspian Basin

Synthetic core from conventional logs: A new method of interpretation for identification of key reservoir properties

2015 ◽  
Vol 3 (1) ◽  
pp. SA1-SA14 ◽  
Author(s):  
Mahbub Alam ◽  
Latif Ibna-Hamid ◽  
Joan Embleton ◽  
Larry Lines
Keyword(s):  
Reservoir Simulation ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Laboratory Data ◽  
Sedimentary Facies ◽  
Rock Properties ◽  
Reservoir Properties ◽  
Particle Volume ◽  
Single Curve ◽  
Conventional Logs

We developed a unique method to generate reservoir attributes by creating an artificial core for those wells that have no core, but that have gamma, neutron, and density logs. We examined sedimentary facies distributions, reservoir attributes, and mechanical parameters of the rock for noncored wells to increase the data density and improve the understanding of the reservoir. This method eventually helps to improve high-resolution 3D geocellular models, geomechanical models, and reservoir simulation in reservoir characterization. Artificial or synthetic cores are created using a single curve that builds facies templates using the information from the cores of nearby offset wells, which belong to the same depositional environment. The single curve, called the fine particle volume (FPV), is the average of two shale volumes calculated from the gamma-ray log and from a combination of neutron and density logs. Using facies templates, the FPV curve builds the synthetic core for geocellular modeling and reservoir simulation, and it represents the sedimentary facies distribution in the well with all the reservoir attributes obtained from laboratory data of the original core. The vertical succession of the synthetic core has the characteristics of actual sedimentary facies with reservoir attributes such as porosity, permeability, and other rock properties. The result of creating the synthetic core was validated visually and statistically with the actual cores, and each of the cored wells was considered as a noncored well. The limitation of this method is associated with the accuracy of the logging data acquisition, normalization factors, and facies template selection criteria.

scholarly journals Crustal density structure of the northwestern Iranian Plateau

2019 ◽  
Vol 56 (12) ◽  
pp. 1347-1365 ◽  
Author(s):  
Vahid Teknik ◽  
Abdolreza Ghods ◽  
Hans Thybo ◽  
Irina M. Artemieva
Keyword(s):  
High Density ◽  
Central Iran ◽  
P Wave ◽  
Arabian Plate ◽  
Scale Model ◽  
Moho Depth ◽  
Caspian Basin ◽  
The South ◽  
South Caspian Basin ◽  
Iranian Plateau

We present a new 2D crustal-scale model of the northwestern Iranian plateau based on gravity–magnetic modeling along the 500 km long China–Iran Geological and Geophysical Survey in the Iranian plateau (CIGSIP) seismic profile across major tectonic provinces of Iran from the Arabian plate into the South Caspian Basin (SCB). The seismic P-wave receiver function (RF) model along the profile is used to constrain major crustal boundaries in the density model. Our 2D crustal model shows significant variation in the sedimentary thickness, Moho depth, and the depth and extent of intra-crustal interfaces. The Main Recent Fault (MRF) between the Arabian crust and the overriding central Iran crust dips at approximately 13° towards the northeast to a depth of about 40 km. The geometry of the MRF suggests about 150 km of underthrusting of the Arabian plate beneath central Iran. Our results indicate the presence of a high-density lower crustal layer beneath Zagros. We identify a new crustal-scale suture beneath the Tarom valley between the South Caspian Basin crust and Central Iran and the Alborz. This suture is associated with sharp variation in Moho depth, topography, and magnetic anomalies, and is underlain by a 20 km thick high-density crustal root at 35–55 km depth. The high-density lower crust in Alborz and Zagros may be related to partial eclogitization of crustal roots below about 40 km depth. The gravity and magnetic models indicate a highly extended continental crust for the SCB crust along the profile. Low observed magnetic susceptibility of the Kermanshah ophiolites likely indicates that the ophiolite rocks only form a thin layer that has been thrust over the sedimentary cover.

Elemental analysis using natural gamma-ray spectroscopy

1994 ◽  
Vol 353 (1-3) ◽  
pp. 558-561 ◽  
Author(s):  
A. Aksoy ◽  
A.A. Naqvi ◽  
F.Z. Khiari ◽  
F. Abujarad ◽  
M. Al-Ohali ◽  
...  
Keyword(s):  
Elemental Analysis ◽  
Gamma Ray ◽  
Natural Gamma ◽  
Gamma Ray Spectroscopy

Bayesian deconvolution of gamma‐ray logs

Geophysics ◽  
1987 ◽  
Vol 52 (11) ◽  
pp. 1535-1546 ◽  
Author(s):  
Ping Sheng ◽  
Benjamin White ◽  
Balan Nair ◽  
Sandra Kerford
Keyword(s):  
High Frequency ◽  
Gamma Ray ◽  
Simulated Data ◽  
Field Measurements ◽  
Bayesian Optimization ◽  
Frequency Noise ◽  
High Frequency Noise ◽  
Data Application ◽  
Gamma Ray Detector ◽  
Gamma Ray Log

The spatial resolution of gamma‐ray logs is defined by the length 𝓁 of the gamma‐ray detector. To resolve thin beds whose thickness is less than 𝓁, it is generally desirable to deconvolve the data to reduce the averaging effect of the detector. However, inherent in the deconvolution operation is an amplification of high‐frequency noise, which can be a detriment to the intended goal of increased resolution. We propose a Bayesian statistical approach to gamma‐ray log deconvolution which is based on optimization of a probability function which takes into account the statistics of gamma‐ray log measurements as well as the empirical information derived from the data. Application of this method to simulated data and to field measurements shows that it is effective in suppressing high‐frequency noise encountered in the deconvolution of gamma‐ray logs. In particular, a comparison with the least‐squares deconvolution approach indicates that the incorporation of physical and statistical information in the Bayesian optimization process results in optimal filtering of the deconvolved results.

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