Vertical variability of geochemical, petrophysical, and rock mechanical properties in upper Cretaceous organic-rich carbonate source rocks, Jordan

Spectral gamma ray (SGR) logs are used as stratigraphic tools in correlation, sequence stratigraphy and most recently, in clastic successions as a proxy for changes in hinterland palaeoweathering. In this study we analyse the spectral gamma ray signal recorded in two boreholes that penetrated the carbonate and evaporate‐dominated Permian–Triassic boundary (PTB) in the South Pars Gasfield (offshore Iran, Persian Gulf) in an attempt to analyse palaeoenvironmental changes from the upper Permian (Upper Dalan Formation) and lower Triassic (Lower Kangan Formation). The results are compared to lithological changes, total organic carbon (TOC) contents and published stable isotope (δ18O,δ13C) results. This work is the first to consider palaeoclimatic effects on SGR logs from a carbonate/evaporate succession. While Th/U ratios compare well to isotope data (and thus a change to less arid hinterland climates from the Late Permian to the Early Triassic), Th/K ratios do not, suggesting a control not related to hinterland weathering. Furthermore, elevated Th/U ratios in the Early Triassic could reflect a global drawdown in U, rather than a more humid episode in the sediment hinterlands, with coincident changes in TOC. Previous work that used spectral gamma ray data in siliciclastic successions as a palaeoclimate proxy may not apply in carbonate/evaporate sedimentary rocks. Copyright © 2014 John Wiley & Sons, Ltd.

Central Iran is one of the active mining zones of Iran and has great mining potential. Large iron mines such as Choghart, Chadormalu, Sechahoon, Chahgaz, Lake Siah, Mishdavan, etc. are located in this zone. Other metals also exist in this zone Like lead and zinc in Koushk, Chahmir and Taj-Kooh mines. Also, non-metallic deposits such as Fahraj limestone mines and building stone mines such as Bishedar marble, Taft travertine, Shirkooh granite, etc. are being extracted in this zone. Considering mineral resources and current explorations, the mines continue to develop and one of the important topics in the exploration and exploitation phase is the study of geomechanical conditions in the zone under study. The relationship between the physical and mechanical properties of rocks makes it possible to predict the strength of intact rock which can be used in preliminary designing of the mine at less cost and less time and just with some simple tests on exploratory boreholes and surface samples. It can also be used in mines under extraction to gain more comprehensive knowledge of the mechanical properties of mine rocks. In this study, mechanical properties such as uniaxial compressive strength, point load, indirect tensile strength (Brazilian) as well as physical properties of rock such as density, porosity, compressive wave velocity (P-wave) and electrical resistivity were measured on selected samples taken from Choghart, Sechahoon, Lakeh Siah, Koushk, Bishehdar marble, Taft travertine, Ravar sandstone and the cores of 5 geotechnical boreholes from the Anomaly VI of Central Iran Iron Ore and 4 geotechnical boreholes of Chahgaz iron ore mine. The purpose of these measurements is investigating the relationship between mechanical and physical properties of the samples, especially electrical resistivity. In the first step, 300 surface and depth samples were collected from the mines mentioned above. After preparing the cores, effective porosity and density were recorded according to the standards (weighing the saturated and dry sample method). Also, the electrical resistivity was calculated by measuring the voltage and electrical current in the samples. The results demonstrated that there is a high correlation between P-wave velocity and electrical resistivity in all the samples. Furthermore, both parameters of P-wave velocity and electrical resistivity are dependent on porosity, and electrical resistivity like P-wave velocity, has a good relationship with mechanical properties of sedimentary rocks and volcano-sediments. Hence, the special electrical resistivity can be used as a non-destructive test to estimate the mechanical properties of rocks. Additionally, the presence of metal ores in the samples in low percentages does not cause errors in estimating physical and mechanical parameters as long as density is less than 2.8 gr/cm3. For samples with high metal content, induced polarization measurements can reduce uncertainty of the electrical resistivity.

Hydrocarbon source rock evaluation and quantification of organic richness from correlation of well logs and geochemical data: A case study from the sembar formation, Southern Indus Basin, Pakistan

The basement of the Gonghe Basin complex (GBC) mainly consists of plutonic rocks, which, in general are suitable for geothermal applications. Knowledge of the rock properties of the deep basement formations is of fundamental importance for unconventional geothermal applications such as enhanced geothermal systems. An outcrop analogue study at the margin of the GBC was conducted to improve the understanding of the petrophysical rock properties and enhance the data availability for numeric simulation and resource assessment approaches. In total 148 samples were derived from 21 sampling locations at the margin of the GBC area and mountain ranges within. Lithologically, the sample set was divided in three sample types: (1) syenogranite, (2) granite and biotite granite, (3) granodiorite. Petrophysical properties such as grain density, bulk density, porosity, intrinsic matrix permeability, compressional and shear wave velocities as well as thermal properties like thermal conductivity and thermal diffusivity were analyzed on oven-dry specimens under laboratory conditions (ambient temperature, atmospheric pressure). Unconfined compressive strength was additionally measured on selected samples. The resulting dataset shows averaged bulk densities ranging between 2.59 and 2.73 g cm−3 and porosities from 0.2 to 1.7%. Matrix permeability is lower than 1 × 10–18 m2. Averaged thermal conductivity ranges from 2.34 to 3.19 W m−1 K−1, compressional wave velocity from 3.6 to 6.2 km s−1 and unconfined compressive strength from 128 to 241 MPa. Petrophysical data are correlated with mineral content and grain size to show the influence of petrography on petrophysical properties. Although the petrophysical rock properties were analyzed at laboratory conditions and therefore deviate from in situ properties at reservoir conditions, the presented dataset enhances the knowledge of petrophysical rock properties within the study area for further geothermal applications. A first prediction of in situ reservoir conditions was performed on laboratory data based on empirically determined pressure and temperature dependencies of thermal conductivity, thermal diffusivity, specific heat capacity and compressional wave velocity.

Stratigraphic correlation in mudstone intervals is challenging as compared to coarser-grained sedimentary rocks because of the microscale heterogeneity and other constraints. Given critical mm- to cm-scale variability in mudstones, it is daunting to try to infer compositional variability from well logs and seismic data unless core data and laboratory analyses are available to calibrate the results. In this study, we propose a novel integrated approach combining sedimentological core description with geochemical data to establish chemofacies and chemostratigraphic zonation using a set of unsupervised statistical tools, i.e., Principal Component Analysis (PCA) and Hierarchical Clustering on Principal Components (HCPC). These techniques can be applied to elemental data acquired using x-ray fluorescence measured from core or cuttings samples or spectroscopy logs to provide robust analysis for unconventional assessment regarding sweet-spot identification, sequence stratigraphic interpretations, and drilling and completion designs. Further, the identified zones can be used to characterize/correlate zones in nearby un-cored wells, with the data generated serving as an indispensable input for establishing a well-log data zonation using unsupervised machine learning algorithms.

As with shales, the source potential of carbonate rocks depends primarily upon the organic facies rather than the mineral matrix. Where the depositional and early diagenetic environment is highly oxygenated, the total organic carbon (TOC) is low, with a negligible generative capacity for hydrocarbons, despite a relatively high hydrocarbon/TOC ratio in the immature state. An anoxic depositional and early diagenetic environment can result in the deposition of organic-rich, fine-grained carbonate rocks that are excellent potential source rocks. Excellent oil-prone source rocks, whether with carbonate or clay mineral matrices, have many characteristics in common. Both form in anoxic environments, are generally laminated and heterogeneous, have moderate to high TOC, and contain high quality organic matter (OM). Gas-prone organic facies are rare in carbonate rocks because such facies are usually dominated by terrestrial organic matter deposited in a dominantly clay matrix. Most carbonate rocks contain nongenerative organic facies as do most siliceous rocks. Oxygen-rich depositional environments for carbonates are found from sea level (reefs) to the ocean depths (Globigerina ooze). Despite the basic commonality between organic-rich oil-prone carbonate and shale source rocks, some significant differences exist. Oils derived from carbonate rocks are often richer in cyclic hydrocarbons and sulfur compounds than oils derived from shales due to the dearth of terrestrial plant waxes in the OM and less iron in the pore water. In addition, the generally earlier decrease of porosity and permeability and the greater contrast between the physical properties of the OM and the rock matrix in carbonate source rocks often result in different primary migration characteristics. End_of_Article - Last_Page 494------------

Shear and Compressional Wave Velocities along with other Petrophysical Logs, are considered as upmost important data for Hydrocarbon reservoirs characterization. Shear Wave Velocity (Vs) in Well Logging is commonly measured by some sort of Dipole Logging Tools, which are able to acquire Shear Waves as well as Compressional Waves such as Sonic Scanner, DSI (Dipole Shear Sonic imager) by Schlumberger and MDA (Monopole-Dipole Array) by Weatherford Company. Usually in Old Wells, there is lack of Shear Velocity data, or in other Wells, only some intervals may have Vs data. Shear Wave Velocity is of high importance in Geophysical studies such as AVO (Amplitude Variation with Offset) and VSP (Vertical Seismic Profiling) and along with Compressional Wave Velocity, it can be used for identification of Fluid Type, Lithology and Mechanical Rock Properties. Genetic Algorithms Technique as a subset of Evolutionary Computing is an important part of Intelligent Systems for solving Optimization Problems. In this study, Compressional and Shear Wave Velocities were modeled by Genetic Algorithms Technique in Ghar member of Asmari Formation, Hendijan Field. For measuring the accuracy of the method, predicted values were compared with the real data in Ghar member of Asmari Formation, Abuzar Field.

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development. The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies. Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well. In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.

A new method has been developed to predict the undrained shear strength from geophysical logs and core measurements using neural networks. A limited number of undrained shear strength measurements are performed on cores, resulting in a limited, discontinuous data set. A series of neural networks were developed to predict the undrained shear strength of the sediments where measurements were not available. The prediction is based on a learned relationship between the suite of geophysical logs and the measurements of undrained shear strength. The use of neural networks to predict the undrained shear strength in cores requires that a statistically significant number of shear strength measurements are available to train and test the neural network. Geophysical logs are necessary to develop a relationship with the shear strength measurements. Shear strengths were measured on core samples in intervals ranging from 15 cm to greater than 200 cm. The neural network was able to resolve the shear strength measurements to a depth resolution of approximately 8 cm. The predicted undrained shear strength values, when tested against actual shear strength measurements, show good correlation. Introduction The expense involved in drilling and coring marine sediment in the world's oceans for scientific research is high. Testing and sampling of each core section must be prioritized and shared within the scientific community. Non-destructive testing of sediment core complements sediment sampling, accessing more information from each core section than from sampling alone. The multi-sensor core logger (MSCL) is an instrument that non-destructively measures the physical properties of sediments, such as bulk density, resistivity, compressional wave velocity and magnetic susceptibility at very high depth resolution. Each core section from the Integrated Ocean Drilling Program (IODP) and the Ocean Drilling Program (ODP) is run through the MSCL. In addition to the MSCL, in-situ non-destructive measurements are made through downhole wireline logging. Wireline logs give a continuous record of several geophysical properties. Traditionally, however, measurements of the undrained shear strength (Su) of a sediment require that the test disturbs the sediment. To maximize the amount of undrained shear strength data available from a borehole, a neural network computer program was used to estimate Su values based on a learned relationship between Su measurements and the MSCL and downhole wireline log data available. If only a limited number of Su measurements are possible, the neural network program is used to fill in the gaps of Su data where measurements were not possible. Neural Network Overview A neural network is a pattern recognition or function approximation computer program that emulates the learning and predictive behavior of the human brain, in which future responses or actions are dictated by the outcome of previous experiences (Haykin, 1994). A typical neural network consists of a series of input data, one or more hidden layers of neurons that give a weight to the data inputs, gauging their importance, and an output layer.

Comparative study on engineering properties of cement-based backfill material for sustainable urban area

The integration of core based rock mechanics with the open hole log and seismic geophysics technologies, has created added value through operational costs savings for Chevron's world-wide drilling and completion holdings. Early development of this integrated technology focused on supplementing core measured rock property data with open hole log measurements to obtain rock mechanical property estimates throughout the horehole. Log derived mechanical property estimates have lead to the development and worldwide use of the following applications:formation drillability assessment for bit optimization and performance prediction,wellbore stability for extended reach and horizontal wells,sanding predictions for completion design, andformation stress profiling and elastic properties modeling for hydraulic stimulation design. The characterization of rock mechanical properties, in the absence of core, hinges upon the newly created capability of predicting formation shear wave propagation velocities. This breakthrough technique was first developed to estimate formation shear wave velocities for single well (1D) mechanical property predictions using conventional open hole measurements- (compressional wave velocity and bulk density). The technique was then enhanced to utilize surface seismic data sets in a 1D sense to predict formation mechanical properties at undrilled prospect locations. The links to the seismic world have proven particularly valuable for rank wildcat and step out drilling applications where offset well control is absent or extremely limited. The latest innovation has been to characterize volumetric (3D) formation mechanical properties from 3D seismic data cubes. Volumetric rock properties currently determined include Poisson's ratio, horizontal stress magnitude, borehole breakdown pressure gradient, pore pressure gradient and unconfined compressive strength. This paper summarizes the shear wave velocity estimation technique and presents several examples. Field examples will also be shown that demonstrate successful applications of log derived mechanical properties for rock strength and minimum horizontal stress profiling applications for fracture stimulation design. A full set of volumetric formation mechanical property cubes will then be presented, demonstrating the feasibility of using 3D seismic data for well planning. P. 241

Carbonate source rock with low total organic carbon content and high maturity as effective source rock in China: A review

Despite the dramatic growth of exploration and appraisal activities for coalbed methane (CBM) wells in the Bowen basin, there is still a lack of understanding on the impacts of geomechanics toward well planning, drilling issues caused by borehole stability, and production performance. From the regional core rock mechanics analysis performed so far, we have observed that coals are generally weak and are prone to failure that can lead to significant consequences in drilling and completion for CBM production wells. The geomechanics issues become prominent, especially for long reach and complex multibranched laterals wells, as rock mechanical properties vary along the coalseam target. Proper characterization of rock mechanical properties, especially the unconfined compressive strength (UCS), is essential in wellbore stability analysis. In this paper, we focus on geomechanics laboratory core analysis techniques to characterize UCS, challenges in UCS core testing on coal samples, and a case study which compares results from five different types of UCS testing on coal and siltstone samples: conventional uniaxial compression, pseudo-UCS, single stage triaxial, multi-stage triaxial, and scratch. Introduction Wellbore stability issues have significant impact in drilling and completing CBM wells in the Bowen basin, especially in long-reach and complex multi-branched lateral wells. Geomechanics evaluation can be performed to mitigate wellbore stability issues by optimizing the mud weight required to keep a stable borehole (Puspitasari et. al., 2014). Done correctly, this type of evaluation has the potential of preventing costly short- and long-term drilling and completion problems. Proper characterization of rock mechanical properties is essential in wellbore stability analysis. The unconfined compressive strength (UCS) is the most important rock strength parameter to describe stability at the borehole wall (Fjaer et al., 2008). UCS is defined as the strength of a rock or soil sample when crushed in one direction (uniaxial) without lateral restraint (Allaby & Allaby, 2014). A continuous UCS profile along a wellbore is typically estimated using mathematical and empirical equations that uses well logs (such as sonic, density, and GR log) and/or interpreted petrophysical parameters (such as volume of clay, total porosity, and effective porosity). To ensure that the log-derived UCS is representative of the actual rock strength, the equations must be calibrated with direct measurement from laboratory core tests. Laboratory Core Tests to Characterize Rock Strength Several types of laboratory test can be performed to characterize rock strength. The tests are typically done on cylindrical samples (core plugs) obtained at the depth of interest. The size of the plug is typically 1.5 in. in diameter and 3 in. long. For small grain-size rock, such as shales, smaller plug sizes can be used. The small shale plug size is ideally 1 in. in diameter and 2 in. long, but it can also be as little as 0.75 in. in diameter and 1.5 in. long. Table 1 compares of some of the most common tests used to measure UCS in the industry.

Experimental study on the variation of physical and mechanical properties of rock after high temperature treatment

A laboratory study was conducted to develop a database and models for predicting of unconfined compressive strength of rocks in the new administrative capital of Egypt. In this respect, the present study presents correlation equations between the unconfined compressive strength and some mechanical and physical properties of rocks. More than 249 of specimens are prepared and tested; the tests were conducted on four rock types, including basalt, limestone, sandstone, and siltstone. Based on results obtained from the following mechanical and physical tests that were performed on rock samples, unconfined compressive strength, Schmidt hammer, and Brazilian splitting test, they were conducted to determine the mechanical properties of rock specimens, water absorption, and porosity. They were conducted to determine the physical properties of rock specimens. The results of this study indicated that the correlation between unconfined compression strength and the Schmidt hammer test increases as the rock strength. The relationship is found in a polynomial equation. It has been found that the strong linear correlation between unconfined compression strength and Brazilian splitting test for the tested rocks. From this study, it has been found that there is an inverse relationship between the unconfined compression strength and water absorption. This relation is a power equation. It has been found from the present study that the unconfined compression strength increases as the porosity in the rock decreases. The relation is found to be a polynomial equation. The correlation coefficient (R2) varies between 0.5 and 0.95 in all relations.