Elemental Capture Spectroscopy Research Articles

A New Workflow for Estimating Reservoir Properties With Gradient Boosting Model and Joint Inversion Using MWD Measurements

Triple-combo logs are important measurements for estimating geological, petrophysical, and geomechanical properties. Unfortunately, wireline and advanced logging-while-drilling (LWD) logs are typically dropped from the formation evaluation plan for unconventional wells due to economic constraints or borehole instability risks. Available measurements are typically measurement-while-drilling (MWD) gamma ray (GR) logs, along with surface measurements such as weight on bit (WOB), rate of penetration (ROP), torque, rotation per minute (RPM), and differential pressure. The development of a robust and rapid model for predicting reservoir properties using this limited data set would be of high value for geological evaluation. Estimating such properties is a challenging task due to the nonlinear relationship between the available log data and unknown reservoir properties. A novel workflow that combines two sequential models is presented. First is a machine-learning (ML) algorithm to predict triple-combo logs from drilling dynamics and GR logs. To train the ML algorithm, well logs obtained from multiple wells located in the Eagle Ford and Permian Basins are scrutinized to identify important features. This process includes depth shifting, outlier detection, and feature selection, which allows for strategic hyperparameter tuning. Several regression algorithms are investigated, and it is found that gradient boosting algorithms yield superior prediction performance. Unlike random forest methods, boosting algorithms train predictors sequentially, each trying to correct its predecessor. After triple-combo logs are predicted from MWD logs, a physics-based joint inversion model is applied to estimate various reservoir properties. The trained model is deployed on a blind test well, and the predicted logs show excellent agreement compared to the corresponding triple-combo measurements. The multimineral inversion using predicted triple-combo logs yields a geologic model that is validated with elemental capture spectroscopy (ECS) measurements. Additionally, reconstructed logs from the forward model closely match measured logs by minimizing the cost function. Therefore, real-time estimated geological, petrophysical, and geomechanical properties can reveal complex geologic information and be used to mitigate uncertainty related to drilling optimization, reservoir characterization, development planning, and reserve estimation. Using the MWD logs to predict triple-combo logs followed by a joint inversion is an innovative approach for geological evaluation with a limited data set. The developed workflow can successfully provide (1) geologic lithofacies identification and rock typing, (2) more confidence in real-time drilling operation, (3) reservoir properties prediction, (4) missing log imputations and pseudo-log generation with forward modeling, (5) guidance for future logging and perforation, (6) reference for seismic quantitative interpretation (QI) and well tie, and (7) potentially massive computation time saving from days to minutes.

Identification of Shale Lithofacies from FMI Images and ECS Logs Using Machine Learning with GLCM Features

The identification of sedimentary structures in lithofacies is of great significance to the exploration and development of Paleogene shale in the Boxing Sag. However, due to the scale mismatch between the thickness of laminae and the vertical resolution of conventional wireline logs, the conventional lithofacies division method fails to realize the accurate classification of sedimentary structures and cannot meet the needs of reservoir research. Therefore, it is necessary to establish a lithofacies identification method with higher precision from advanced logs. In this paper, a method integrating the gray level co-occurrence matrix (GLCM) and random forest (RF) algorithms is proposed to classify shale lithofacies with different sedimentary structures based on formation micro-imager (FMI) imaging logging and elemental capture spectroscopy (ECS) logging. According to the characteristics of shale laminae on FMI images, GLCM, an image texture extraction tool, is utilized to obtain texture features reflecting sedimentary structures from FMI images. It is proven that GLCM can depict shale sedimentary structures efficiently and accurately, and four texture features (contrast, entropy, energy, and homogeneity) are sensitive to shale sedimentary structures. To accommodate the correlation between the four texture features, the random forest algorithm, which has been proven not to be affected by correlated input features, is selected for supervised lithofacies classification. To enhance the model’s ability to differentiate between argillaceous limestone and calcareous mudstone, the carbonate content and clay content calculated from the ECS logs are involved in the input features. Moreover, grid search cross-validation (CV) is implemented to optimize the hyperparameters of the model. The optimized model achieves favorable performance on training data, validation data, and test data, with average accuracies of 0.84, 0.79, and 0.76, respectively. This study also discusses the application of the classification model in lithofacies and production prediction.

Accurate Petrophysical Interpretation of Carbonate using the Elemental Capture Spectroscopy (ECS)

Elemental capture spectroscopy (ECS) is an important tool in the petroleum industry for determining the composition and properties of rock formations in a reservoir. Knowledge of the types and abundance of different minerals in the reservoir is crucial for accurate petrophysical interpretation, reservoir engineering practices, and stratigraphic correlation. ECS measures the elemental content of the rock, which directly impacts several physical properties that are essential for reservoir characterization, such as porosity, fluid saturation, permeability, and matrix density. The ability to accurately determine these properties leads to better reservoir mapping, improved production, and more effective resource management. Accurately determining the mineralogy and porosity of carbonate rocks and other materials is the aim of this paper. Calcite, dolomite, quartz, clay (illite), anhydrite, and pyrite, in addition to water as a fluid, are taken into account in the computation. The formation's lithology and porosity can be ascertained from this data. When compared to the core descriptions in the geological report, the results demonstrated a distinct zone of unique lithology with good prediction accuracy.

Formation lithology predictions based on measurement while drilling (MWD) using gradient boosting algorithms

Many subsurface engineering applications require petrophysical and geomechanical properties as inputs for estimating the recoverable hydrocarbon volume and the reservoir deformation and properties' change during the life cycle of the production. Well log data plays a key role in estimating these reservoir properties, including volumes of minerals, Young's Modulus, and Poisson's Ratio. A conventional workflow using a multi-mineral petrophysical inversion model based on core-log integration requires an a priori rock-fluid model; therefore, it is often unavailable in reservoirs of complex minerals, such as carbonate or unconventional fields. In addition, most unconventional wells do not acquire Logging While Drilling (LWD) and/or wireline logs due to economic constraints or borehole instability reasons. Available measurements for these wells are typically Measurement While Drilling (MWD) Gamma Ray (GRMA) logs along with drilling dynamics measurements including Surface Weight-On-Bit (SWOB), Rate Of Penetration (ROP), Torque (TQA), Revolutions Per Minute (RPM), and the differential pressure. Thus, in this study, we demonstrate a machine learning approach, which does not require an a priori model, to predict formation lithologies based on conventional MWD and wireline well logs.We applied the Extreme Gradient Boosting Regression (XGBR) method to simultaneously predict the weight fractions of 5 minerals: QFM (Quartz-Feldspar-Mica), carbonate, clays, pyrite, and siderite from drilling dynamics measurements. The XGBR algorithm was tested with datasets from four adjacent wells in the Volve Field, located in the North Sea. A total of 10,575 data points were used to train and validate the model. The trained model was then applied to the blind test dataset of 10,446 data points from the same field. Lithology predictions from the trained model yield excellent results of weight concentrations with Root-Mean-Square Errors (RMSE) of 6.01 and 7.44% for training and test datasets, respectively. The prediction results agree with the lithology obtained from the Elemental Capture Spectroscopy (ECS) data as well as the dominant lithology record from rock cuttings. With the developed model, lithologic information and sonic logs of the formation could be predicted with MWD data, which could ultimately help optimize real-time drilling and completion operations in the field.

Rock physics based velocity-porosity correlations developed for estimation of the elastic properties of the Bakken formations of the Williston Basin, North Dakota

Rock physics models are predictive tools used to estimate the velocity, or elastic properties of formations, based on strong theoretical foundations, as opposed to some of the simple empirical correlations that have been developed for specific regional formations. While the basic rock physics models use simplified assumptions and involve less input, more complex models, such as inclusion-based models, estimate velocity-porosity relation as function of different parameters including pore's fluid saturation and type, cementation, confining pressure and diagenesis. The downside is that these models require performing tedious calculations and in some cases solving complex differential equations. These models may also differ depending on the type of formation, for example, separate models may be used for shales compared to the siliciclastic or carbonate formations.In this paper, we present newly developed correlations that can substitute the rock physics modeling. The developed velocity-porosity correlationsare based on the mineralogical data from the Bakken formation in the Williston Basin, North Dakota, however, they can be used with good accuracy for other formations too. These models were developed based on large volumes of data from simulation of many cases using the differential effective medium (DEM) theory, a commonly used rock physics model. DEM models were developed for composite rock, but assuming single mineral type constituting the rock. The inclusion phase was incrementally added to the matrix phase. The pores were then modeled with three phase fluid (water, oil, and gas) at different saturation levels and finally, different pore's aspects ratios (AR) were assumed to simulate pores heterogeneity (i.e., crack, intraparticle, intergranular and moldic type pore geometries). The correlations constants were extracted for different key minerals in the Bakken, including quartz, calcite, dolomite, anhydrite, illite and kerogen. Having the volume fraction of minerals from lab-based XRD or Elemental Capture Spectroscopy (ECS) logs, linear averaging was applied to estimate the equivalent velocity and elastic properties of the formations.The correlations were applied to several case studies in the Bakken and also compared with the existing lab data, which showed a good agreement with the DEM model. The simplicity of using the correlations consists of simply entering the input values in an excel template that accounts for the constant parameters, using a single approach for different type of formations. This offers a great advantage for their applications. The workflow of developing the correlations and its applications in one case stduy using data from a Well in the Mountrail County will be presented.

Adaptability analysis and optimization of log suite for uncased wellbores of shale gas reservoirs: A case study of Sichuan Basin, China

Significant progress has been made in the exploration and development of unconventional gas resources in China since the beginning of the 21st century. With a rapid increase in yield, the exploration and development of shale gas have been upgraded to a level of national strategy. Logging is one of the core technologies in gas exploration and development. However, logging technologies face the challenges of complex geological conditions and well casing environment, high temperature, high pressure, and strong heterogeneity. Despite the rich experience accumulated, unified understanding and implementation specifications are yet to be established for shale gas well logging. Given the analysis and comparison of the effectiveness and adaptability of logging technologies at different stages of shale gas exploration and development for the Sichuan Basin, this study optimizes the log suite to meet the demand for high-efficiency exploration and development of shale gas. According to the adaptability analysis of shale gas log suite, the mandatory logging items of exploratory wells should include caliper log (CAL), natural gamma ray spectrometry (NGS) log, spontaneous potential (SP) log, directional survey, borehole compensated sonic log, litho-density log (LDL), compensated neutron log (CNL), dual laterolog-microspherically focused log/dual induction-laterolog log/array induction log, temperature log, elemental capture spectroscopy (ECS) log, formation microimager (FMI) log, and array sonic log. The log suite of appraisal wells is almost the same as that for exploratory wells, excluding the ECS log and micro-resistivity scanning imaging. Meanwhile, for the logging of horizontal/highly-deviated holes, the log suite of vertical wells should be referred to according to specific well conditions.

Lithologic classification of pyroclastic rocks: A case study for the third member of the Huoshiling Formation, Dehui fault depression, Songliao Basin, NE China

With the ongoing discovery of volcanic oil and gas reservoirs, pyroclastic rocks have begun to attract widespread attention from both the academic and industrial sectors. Because there is more information about the cementation mode and the grain size in the Chinese classification system for pyroclastic rocks than there is in the system used throughout the rest of the world, we use the Chinese classification system to investigate the lithologies of the pyroclastic rocks in our study area. The three cementation modes dictate the three classes of pyroclastic rock: pyroclastic lava, normal pyroclastic rock, and sedimentary pyroclastic rock. Within these three modes, pyroclastic rocks are further classified by their clastic material grain size into agglomerate, breccia, and tuff. If the pyroclastic material is moved by aeolian transport, it tends to follow the positive sedimentation cycle sequence. In this study, we classify the pyroclastic rocks from the third member of the Huoshiling Formation (Fm) in the Dehui fault depression in the southern Songliao Basin. Draw the cross plots of pyroclastic lava, normal pyroclastic rock and sedimentary pyroclastic rock in the study area. Based on core date, fullbore micro-resistivity images (FMIs), and thin section analyses, we determine the mineral composition, particle size, and cementation mode of breccia bearing tuff lava, tuff lava, breccia bearing welded tuff, volcanic breccia, breccia bearing tuff, tuff, sedimentary volcanic breccia, and sedimentary tuff. Our results indicate that the decision tree method does not require all of the input curves to identify a given sample's lithology. We also found that adding more data to a support vector machine (SVM) does not necessarily translate into more accurate classification results. This classification is undertaken using two different techniques: a combination of elemental capture spectroscopy (ECS) cross plots and FMI and a SVM learning algorithm with information derived from node information in a decision tree. Ultimately, the results produced by the SVM algorithm are more accurate (recognition accuracy of 98.6%) than those generated by the ECS/FMI approach. Furthermore, the SVM algorithm requires less data and operates very efficiently. Using data from multiple wells allows for better and more rapid identification of the lithology type in the target layer; large observational or training datasets provide a strong foundation for further reservoir evaluation.

Spearman rank correlations analysis of the elemental, mineral concentrations, and mechanical parameters of the Lower Cambrian Niutitang shale: A case study in the Fenggang block, Northeast Guizhou Province, South China

The increasing complexity of reservoirs, particularly shale reservoirs, demands accurate information on the formation composition, mineralogy, and mechanical parameters for effective exploitation. The development of geochemical and geophysical logging technology allows the investigation of the correlations between the elemental, mineral contents, and the mechanical parameters of shale. Taking the Lower Cambrian Niutitang Formation shale in the Fenggang block in northeastern Guizhou as an example, 889 data sets of the main elemental (Si, Ca, Fe, S, Ti, Gd, K, Mg, and S) concentrations and minerals (carbonate, pyrite, clay, quartz, feldspar, and mica) from Elemental Capture Spectroscopy logging and rock mechanics parameters (dynamic Young's modulus, dynamic Poisson's ratio, shear modulus, bulk modulus, and brittleness index) determined via Sonic Scanner logging data in the target zone of the study area are collected. We investigated the relationships between the mechanical parameters and the elemental, and mineral concentrations by Spearman rank correlation analysis. The influence of different Si concentrations on the mechanical parameters of the rock (positive/negative, and significance) is discussed based on the mineral contents determined via X-ray diffraction of 24 experimental samples. When 22.1% < DWSi (dry weight fraction of the silicon content as a percent) < 28.40% and 28.40% < DWSi <34.20%, the static Young's modulus, shear modulus, bulk modulus, and brittleness index exhibit a highly positive/negative correlation that is significant with the increase/decrease in DWSi, WCLA (dry weight fraction of clay), and WQFM (dry weight fraction of quartz, feldspar, and mica) contents, respectively. DWSi = 28.4% is suggested as the turning point for the correlation change (positive/negative, significance). Thus, there exists uncertainty in the determination of the mechanical parameters of rock within certain elemental content ranges. More mathematical models and factors need to be considered to adequately characterize the mechanical parameters of rock.

Integrated Petrophysical Evaluation of Shale Gas Reservoirs for Wufeng-Longmaxi Formation in Southeast Chongqing

Shale gas is an important unconventional natural gas resource. Different from conventional oil and gas exploration, gas shale is not only the source rock of natural gas, but also the cap rock and reservoir. This paper presents a logging interpretation method for comprehensive evaluation of shale gas. Taking well A in southeast Chongqing from Sichuan Basin as an example, the shale gas reservoir of Wufeng-Longmaxi formation is evaluated. This method makes full use of conventional logging data (gamma ray, array lateral resistivity, density, neutron, acoustic, spectral gamma ray), unconventional log data (NMR Nuclear Magnetic Resonance, ECS Elemental Capture Spectroscopy) and core experiment data (X-ray Diffraction, Tight Rock Analysis, Methane Adsorption Isotherm). This method of combining logging data with core measurement can provide mineralogy, porosity, permeability, saturation, kerogen volume, total organic carbon content, absorbed gas content and free gas content. The methods provide a means to understand the petrophysical properties and reservoir quality of Wufeng-Longmaxi shale. This information can help to determine the “sweet spot” along the vertical to land and design a selective completion strategy to optimize well productivity.

Rock classification in the Eagle Ford Formation through integration of petrophysical, geological, geochemical, and geomechanical characterization

Integration of geomechanical, geological, geochemical, and petrophysical characterization is critical to enhance production from organic-rich mudrocks. This paper introduces an integrated rock classification method in the Eagle Ford Formation shales and marls in southern Texas, consisting of organic-rich fossiliferous marine shale deposited during the Late Cretaceous. A joint inversion of triple-combo, spectral gamma-ray, and elemental capture spectroscopy logs was initially conducted to estimate volumetric concentrations of minerals, porosity, and fluid saturation. Effective elastic properties such as Young's modulus and Poisson's ratio as well as minimum horizontal stress were then estimated as a function of depth. Rock classification was finally performed based on geologic texture and geochemical properties, as well as well log-based estimates of petrophysical and geomechanical properties. The introduced method was applied to two wells located in the oil window of the Eagle Ford Formation (average gas-to-oil ratio of less than 2000 MCF/STB). The results of the integrated rock classification demonstrated similar organic richness and petrophysical properties in both wells. However, the geomechanical rock classification derived from in-situ stress profiles suggested higher proportions (55% vs. 34% of net thickness, respectively) of the better completions quality rock class, with lower minimum horizontal stress (MHS), in one of the wells. A 90-day report on the cumulative oil production of this well shows an additional 11,000 bbl (i.e., 20% more oil production) compared to the second well. This observation was accounted for by geomechanical properties and the distribution of rock classes that differentiate reservoir quality and production.

An Advanced Well Log and an Effective Methodology to Evaluate Water Saturation of the Organic-Rich Cambay Shale

Water saturation (Sw) is a vital parameter in the evaluation of hydrocarbon reservoir. Realistic estimation of Sw in shale reservoir is a challenging problem because of the presence of significant clay minerals and organic matter. The present study aims to estimate Sw of organic-rich Cambay Shale, Jambusar–Broach block, Cambay Basin, India, using an effective methodology and improved equation (TOC–clay equation), considering the appropriate corrections to counter the effect of clay minerals and organic matter in modifying resistivity. Advanced log like Litho-scanner has been utilized for the continuous estimation of clay content and total organic carbon (TOC), required for the estimation of Sw. Elemental capture spectroscopy and support vector regression were also applied for evaluation of clay content and TOC in one well where Litho-scanner log is not available. To investigate the effectiveness and applicability of the TOC–clay equation, results of Sw estimated from this equation and various other models have been compared with core Sw for a well. The analysis indicates that Sw derived from TOC–clay equation is having the closest agreement to core Sw data with an average absolute percentage error of 7.9% and coefficient of correlation 0.95. For other models, average absolute errors are significantly higher (16.8–32.7%). The application of TOC–clay equation will be useful for evaluation of the Cambay Shale reservoir. In addition, the resistivity-based TOC–clay equation used in this study is simple with a practical approach; it can be applied easily and efficiently for any organic-rich shale reservoir elsewhere.

Qualitative and quantitative prediction of diagenetic facies via well logs

Drilled cores, thin section petrography, X-ray diffraction (XRD), scanning electron microscopy (SEM), routine core analysis (RCA), and mercury injection capillary pressure (MICP) tests were used to establish a predictable model for diagenetic facies and reservoir quality using well logs in sandstones of the Paleogene Dongying Formation in Nanpu Sag, Bohai Bay Basin, Eastern China. The results show that the sandstones are texturally and compositionally immature, and have primary intergranular pores, secondary intergranular and intragranular pores, moldic pores, and micro-pores associated with diagenetic clay minerals. The sandstones have undergone low to moderate mechanical compaction, but a high degree of framework-grain dissolution. Diagenetic minerals are dominantly of carbonates (calcite, dolomite, and Fe-dolomite) and clay minerals (illite, mixed layer illite/smectite and kaolinite). Four types of diagentic facies are recognized: tightly compacted, carbonate cemented, clay mineral filling, and dissolution facies. Porosity, permeability and oil saturation ranges among these diagenetic facies were determined from routine core analyses. The diagenetic facies were then translated to conventional well logs by core-log calibration, and the well log expressions (in terms of natural gamma ray, neutron porosity, bulk density, sonic transit time, and resistivity logs) of various diagenetic facies were summarized. The mineralogical composition obtained from the Elemental Capture Spectroscopy (ECS) logs was then used for the diagenetic facies prediction. The diagenetic facies predicted with the comprehensive conventional log analyses were combined with the ECS log profile, and were calibrated with the XRD, SEM analysis and logging interpretation results. For wells without ECS log, the diagenetic facies can be quantitatively predicted by the mathematical calculations of reservoir quality index (RQI) via the GR, AC and DEN log curves. Reservoir quality or oil bearing properties can be evaluated and predicted by translating the diagenetic facies to well logs.

Application of Multi-Resolution Graph-based Clustering for electrofacies prediction: a case study from the Horn River Shale, British Columbia, Canada

The Horn River Basin is a major shale gas play in British Columbia, Canada. An important component for identifying productive zones is to determine lithofacies. Quantitative core-based lithofacies classifications distinguish reservoirs and non-reservoir quality zones. In this study, Elemental capture spectroscopy (ECS) log data, XRD, and TOC data were integrated with core-description data to define the shale gas development interval in the Horn River Shale. Six lithofacies were determined and used as a training data for the supervised electrofacies classification. The Multi-Resolution Graph-based Clustering method (MRGC) was useful for prediction of electrofacies. The MRGC facilitated estimation of the electrofacies from the well logs of the non-coring wells based on our current construction model. We constructed a supervised MRGC model from well A. The probability scores of the electrofacies were 83%. This method was applied to non-coring wells B and C. Lateral variations in the facies were inferred using an electrofacies correlation or seismic-scale spatial model. Stochastic methods were applied to build a 3D facies model due to the insufficient electrofacies data. To increase the accuracy and applicability we used seismically derived-trend data (density and P-wave sonic inversion, envelope attributes) for sequential indicator simulation (SIS) modeling. After constructing a facies model, the faintly laminated siliceous mudstone (FLSM) and homogeneous siliceous mudstone (HSM) facies were shown to be dominantly distributed throughout the Evie Member and Muskwa Formation. These facies contain high silica compared to clay and higher TOC contents which are generally the main targets of a shale gas play. Therefore, the FLSM and HSM facies of the Muskwa Formation and Evie Member are potentially productive facies for shale gas development in the Horn River Shale.

Incorporating the effects of elemental concentrations on rock tensile failure

To bridge the gap between elemental concentrations and rock mechanics, this paper proposed to predict a tensile failure using the weight concentrations of various elements. In this research, the Brazilian test was used to measure the indirect tensile strength of sandstones under different environmental conditions, i.e., oven drying, water immersion, and mud filtration. According to the mechanical test results of 37 sandstone specimens, the fracture patterns can be quite complicated due to an interaction of tensile fracture with shear fracture or bending fracture. To improve the prediction accuracy of the statistical analysis, a digital image correlation (DIC) technique was used to capture the dynamic fracturing process and to verify the validity of the Brazilian tests. Finally, only 15 specimens met the principle of the Brazilian test with a valid tensile failure. Using the energy-dispersive system (EDS), a distinct disparity of elemental concentrations was identified between those two sides of a fracture, such as the content contrasts of C, Mg, Al, Si, Ca, Ti, Fe, and Zn. These key elements can be used to identify the fracture positions and predict the rock mechanical properties, e.g., Brazilian test strength, Young's modulus, and Poisson's ratio. The excellent correlation performance, between rock strength parameters and elemental concentrations, proved it applicable to provide another potential function of the Elemental Capture Spectroscopy log in the evaluation of wellbore integrity.

Brittleness in the Devonian Horn River shale, British Columbia, Canada

The Middle and Upper Devonian Horn River Group shale in northeast British Columbia is recognized as a major source of natural gas in Canada. This shale unit is approximately 200–500 m thick and consists of three stratigraphic units in ascending order: Evie Member, Otter Park Member and Muskwa Formation.In this study, Uniaxial Compressive Strength (UCS) tests were conducted on core plugs across stratigraphic units of the Horn River Group. Shear and compressional wave velocities are also measured for the core plugs to establish a relationship between static and dynamic elastic parameters. Brittleness is measured using three independent methods: (1) a hand-held hardness tester is used to measure mechanical hardness along a core; (2) the Elemental Capture Spectroscopy (ECS) log is used to calculate a composition-based brittleness in the same well; (3) Density and sonic logs along with laboratory experiments are used to calculate a brittleness coefficient based on elastic (static and dynamic) parameters.In order to compare brittleness and rock composition, a detailed lithofacies analysis was also conducted. Three major lithofacies were observed in the Horn River Group, including massive mudstones, pyritic mudstones and laminated mudstones. The Muskwa Formation and Evie Member are dominantly represented by massive and pyritic mudstones while the vast majority of the Otter Park Member is represented by laminated mudstones.The three independent measurements of brittleness show similar trends of higher brittleness in Muskwa Formation and Evie Member compared to Otter Park. This indicates that all three methods are suitable in identifying brittle zones in shale formations. The results show a clear relationship between lithofacies and brittleness. Massive mudstones and pyritic mudstones lithofacies show relatively higher brittleness compared to laminated mudstones lithofacies. Additionally, brittleness is influenced by the mineralogy of the rock. Clay content is observed to have the most dominant (inverse) relationship with brittleness coefficients, followed by quartz content. The analysis is further confirmed by calculating the fracability index for the Horn River Group.

The Application of a New Neutron Induced Gamma Ray Spectroscopy Tool in Evaluation the Shale Gas in Fuling Shale Gas Field

Shale gas reservoir is characterized by complex lithology with ultra-low porosity and permeability, which brings many challenges in the formation evaluation. Elemental Capture Spectroscopy Sonde (ECS) can be used to measure the elements and get the formation of the mineralogy. LithoScanner is a newly-generated spectroscopy tool, which can be used to measure both inelastic and capture spectra, providing estimation of more elements, including silicon, calcium, iron, sulfur, titanium, gadolinium, aluminum, potassium, sodium, magnesium, manganese, carbon, etc. LithoScanner can be directly used to measure the total organic carbon content (TOC) in the formation, and provide solutions to the lithology classification and heterogeneous rock analysis (HRA). The changing lithology and borehole rugosity have little influence on the direct measurement of TOC by LithoScanner. LithoScanner shows great advantage in accuracy when comparing with other methods using triple-combo. The systematic lithology classification method in shale gas reservoir using LithoScanner is correlatable in multi-wells. The brittleness index from mineralogy can be applied to hydraulic stimulation. HRA results offered guides in both the coring sample selection and the experiment. The advantages of LithoScanner in shale gas formation evaluation were presented in this paper, and the workflow can also be applied to other reservoirs.

Mineralogical and chemical distribution of the Es3L oil shale in the Jiyang Depression, Bohai Bay Basin (E China): Implications for paleoenvironmental reconstruction and organic matter accumulation

The Es3L (lower sub-member of the third member of the Eocene Shahejie Formation) shale in the Jiyang Depression is a set of relatively thick and widely deposited lacustrine sediments with elevated organic carbon, and is considered to be one of the most important source rocks in East China. We can determine the mineralogy, organic and inorganic geochemistry of the Es3L shale and calculate paleoclimate indexes by using multiple geochemical proxies based on organic chemistry (total organic carbon [TOC] and Rock-Eval pyrolysis), major and trace elements, X-Ray diffraction, and carbon and oxygen isotope data from key wells alongside ECS (Elemental Capture Spectroscopy) well log data. These indicators can be used to analyze the evolution of the paleoenvironment and provide a mechanism of organic matter (OM) accumulation. The Es3L oil shale has high TOC abundance (most samples >3.0%) and is dominated by Type I kerogens. Additionally, the organic-rich shale is rich in CaO and enrichment in some trace metals is present, such as Sr, Ba and U. The positive δ13C and negative δ18O values, high Sr/Ba, B/Ga and Ca/Ca + Fe ratios and low C/S ratios indicate that the Es3L shales were mainly deposited in a semi-closed freshwater-brackish water lacustrine environment. The consistently low Ti/Al and Si/Al ratios reflect a restricted but rather homogeneous nature for the detrital supply. Many redox indicators, including the Th/U, V/(V + Ni), and δU ratios, pyrite morphology and TOC-TS-Fe diagrams suggest deposition under dysoxic to suboxic conditions. Subsequently, the brackish saline bottom water evolved into an anoxic water body under a relatively arid environment, during which organic-lean marls were deposited in the early stage. Later, an enhanced warm-humid climate provided an abundant mineral nutrient supply and promoted the accumulation of algal material. OM input from algal blooms reached a maximum during the deposition of the organic-rich calcareous shale with seasonal laminations. High P/Ti ratios and a strongly positive relationship between the P and TOC contents indicate that OM accumulation in the oil shale was mainly controlled by the high primary productivity of surface waters with help from a less stratified water column. Factors such as the physical protection of clay minerals and the dilution of detrital influx show less influence on OM enrichment.

Petrophysical approach for S-wave velocity prediction based on brittleness index and total organic carbon of shale gas reservoir: A case study from Horn River Basin, Canada

When finding the “sweet spot” of a shale gas reservoir, it is essential to estimate the brittleness index (BI) and total organic carbon (TOC) of the formation. Particularly, the BI is one of the key factors in determining the crack propagation and crushing efficiency for hydraulic fracturing. There are several methods for estimating the BI of a formation, but most of them are empirical equations that are specific to particular rock types. We estimated the mineralogical BI based on elemental capture spectroscopy (ECS) log and elastic BI based on well log data, and we propose a new method for predicting S-wave velocity (VS) using mineralogical BI and elastic BI. The TOC is related to the gas content of shale gas reservoirs. Since it is difficult to perform core analysis for all intervals of shale gas reservoirs, we make empirical equations for the Horn River Basin, Canada, as well as TOC log using a linear relation between core-tested TOC and well log data. In addition, two empirical equations have been suggested for VS prediction based on density and gamma ray log used for TOC analysis. By applying the empirical equations proposed from the perspective of BI and TOC to another well log data and then comparing predicted VS log with real VS log, the validity of empirical equations suggested in this paper has been tested.

Drilling Evolves as Exploration Campaign Moves to Deeper Water Offshore Mexico

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper OTC 25030, “Drilling Evolution of the Ultradeepwater Drilling Campaign in Mexico, Perdido Fold Belt,” by Victor G. Vallejo, Aciel Olivares, and Octavio Saavedra, Pemex, and Juan R. Lopez and Manuel E. Torres, Schlumberger, prepared for the 2014 Offshore Technology Conference Asia, Kuala Lumpur, 25–28.March. The paper has not been peer reviewed. During the 10-year period between 2003 and 2013, Mexico drilled 30 deep- and ultradeepwater wells in water depths ranging from 512 to 2900&amp;#x7f;m. During this exploration period, the learning curve has improved with each subsequent level of difficulty and increased well depth. In order to reach water depths greater than 1500 m, both the wellbore geometry and the real-time data-acquisition strategy have changed. Introduction Fig. 1 shows how wellbore geometry evolved to reach geological targets and how the various hole diameters changed to meet exploration objectives. As seen on the left of the figure, a conventional drilling and data-acquisition approach was applied: drilling a 12¼-in. pilot hole for data acquisition and thereafter enlarging the hole to 17½ or 14¾ in. to set the casing. This enabled real-time logging-while-drilling (LWD) data acquisition and provided a 12¾-in. maximum hole size for wireline data acquisition. However, as water depth increased (right side of Fig. 1), the hole-size requirements for the mechanical well design increased accordingly. In some cases, with shallow geological targets, the required hole size of 22 in. was needed for the casing. This required a strategy to drill an appropriate hole size for LWD and wireline evaluation with minimal dedicated drilling and hole-enlargement trips. The evaluation programs considered realtime data needed to make decisions during the drilling phase and support decisions for a more- comprehensive wireline evaluation program if the zones warranted more extensive evaluation. The comprehensive evaluation logging suites included wireline-logging tools with triaxial resistivity, elemental-capture spectro scopy, nuclear magnetic resonance, vertical seismic profiles, mechanical sidewall coring, and the complete suite of formation-testing services for pressure and fluid sampling. The ultimate goal of the program was to provide accurate volumetrics, physical rock samples, and fluid samples for the booking of hydrocarbon reserves.

Qualitative and quantitative reservoir bitumen characterization: A core to log correlation methodology

Reservoir bitumen is a highly viscous, asphaltene-rich hydrocarbon that can have important effects on reservoir performance. Discriminating between producible oil and reservoir bitumen is critical for recoverable hydrocarbon volume calculations and production planning, yet the lack of resistivity contrast between the two makes it difficult, if not impossible, to make such differentiation using conventional logs. However, the nuclear magnetic resonance (NMR) response in bitumen-rich zones is dominated by short transverse relaxation times ([Formula: see text]) and a low apparent fluid hydrogen index ([Formula: see text]), providing an opportunity to identify the presence of reservoir bitumen. Therefore, NMR logging technology becomes crucial in the characterization of reservoirs in which the presence of bitumen may be of concern. We used NMR and other log data to identify and quantify the occurrence of reservoir bitumen in a carbonate reservoir. A thorough petrophysical evaluation was performed using a full suite of logs, formation pressure measurements, and laboratory core analysis data. We discuss several quick methods to identify intervals with a higher chance of reservoir bitumen presence. The short transverse relaxation times ([Formula: see text]) and consequently lower [Formula: see text] logarithmic mean time values are characteristics of bitumen-rich zones. Another characteristic is low [Formula: see text] in these zones and consequently lower NMR porosity estimates when compared to porosity estimates from the density and neutron tools. We analyzed 2D longitudinal-transverse relaxation time ([Formula: see text]) maps for core samples at different depths to confirm the presence of reservoir bitumen in some wells using laboratory low-field NMR data. We observed a high [Formula: see text] ratio at various depths, which is an indication of high-molecular-weight hydrocarbons. The presence of bitumen at the same depths was confirmed by thin section analysis, and it is the likely cause for failed formation pressure testing attempts at those depth intervals. Partial cleaning of reservoir bitumen-rich core plugs results in helium-injection porosity estimates that are too low, and they are closer to the NMR porosity than to density porosity, the latter being more consistent with actual values. In addition, the grain density (GD) calculated by He injection is significantly lower than the GD estimated from elemental capture spectroscopy and X-ray diffraction techniques. Disregarding these effects complicates the core to log correlation, which is common practice for porosity calculations using the density log. A volumetric rock model was used to reconcile core and log data as well as to calculate the saturation of reservoir bitumen. The methodologies for reservoir bitumen characterization introduced here can be applied successfully in different reservoirs for more reliable and precise reservoir evaluation and production planning.