Total Organic Carbon Estimation Research Articles

Level of thermal maturity estimation in unconventional reservoirs using interval inversion and simulating annealing method

AbstractThis study presents a novel geophysical approach for estimating the level of thermal maturity (LOM) in unconventional hydrocarbon reservoirs using well log data. LOM is a crucial parameter for assessing the hydrocarbon generation potential of source rocks, but it traditionally relies on laboratory measurements of core samples, which can be time-consuming and costly. The proposed method combines two techniques: interval inversion for estimating total organic carbon (TOC) content from well logs and simulated annealing (SA) optimization for deriving LOM from the estimated TOC. The interval inversion method enables accurate TOC estimation by jointly interpreting multiple well logs over depth intervals, overcoming limitations of conventional point-by-point inversion. Using the estimated TOC, the SA algorithm optimizes an energy function related to Passey's empirical TOC-LOM relationship, iteratively finding the optimal LOM value that best fits the well log data. This approach provides a continuous in situ LOM profile along the borehole without requiring core measurements. The effectiveness of the method is demonstrated through case studies on datasets from the North Sea (Norway), the Pannonian Basin (Hungary), and the Kingak Formation (Alaska). The LOM estimates show good agreement with reported maturity levels and allow reliable reservoir characterization. Statistical analysis confirms the robustness and accuracy of the results. By reducing dependence on core data, this integrated inversion-optimization workflow streamlines the reservoir prospecting phase, enhancing operational efficiency. The method holds promising applications across diverse geological settings for cost-effective evaluation of unconventional hydrocarbon plays.

Estimating soil organic carbon and total nitrogen across inhabited and uninhabited islands based on dual simulations in the entire- and sub-areas

Soil total organic carbon (TOC) and total nitrogen (TN) mapping methods have been developed for different ecosystems; however, little attention has been paid to archipelagos comprising inhabited and uninhabited islands. In this study, we proposed a method of dual simulations in the entire- and sub-areas to adequately improve the simulation accuracy of soil TOC and TN in the archipelago. A typical archipelago, Miaodao Archipelago, was selected to demonstrate the method. The dual simulations comprehensively considered the common characteristics of the entire-area and the unique conditions of each sub-area in the archipelago. Through the dual simulations, the accuracy can be viewed in three hierarchies: the entire area (the first hierarchy), the inhabited and uninhabited islands (the second hierarchy), and the single island (the third hierarchy). The simulation accuracies for TOC were increased by 12.03%, 12.03%, and 16.94% in the first, second, and third hierarchies, respectively, compared with the baseline, and those for TN were increased by 7.13%, 9.21%, and 14.72%, respectively. The lowest normalized root mean squared errors for TOC and TN were 0.114 and 0.105, respectively, which were similar to those reported in previous studies that achieved high simulation accuracy in other areas. Considerable differences in soil TOC and TN existed across the inhabited (39.95 g/kg TOC and 3.61 g/kg TN) and uninhabited (63.42 g/kg TOC and 7.08 g/kg TN) islands, which were driven by the great differences in natural conditions and human activities across the two sub-areas. The dual simulations contributed to the further improvement of simulation accuracy in soil mapping, providing a practical method for soil TOC and TN estimation in the archipelago, and revealing the spatial differences in soil TOC and TN across the inhabited and uninhabited islands.

Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models

This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.

Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression

A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r2 values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images.

Pivotal Role of Microbes in Solid Waste Management

LLactic acid bacteria (LAB) are used widely in food microbiology as probiotics and biopreservatives to extend shelf life of food items. Lactic acid produced by LAB strains are precursor of polylactic acid(PLA) that has growing demand as bioplastics that are biodegradable in nature replacing traditional plastics. LAB have the potential to utilize polysaccharides from various sources and produce lactic acid. The aim of this study is to identify lactic acid producing strains of lactic acid bacteria(LAB) that have efficiency in utilizing biodegradable fragment of domestic solid waste collected from residential areas of Coimbatore municipality for three days. The main objective of this study is to convert the biodegradable solid waste into lactic acid, a precursor of Poly lactic acid- PLA used as bioplastic. Important chemical parameters of the solid waste like phosphate estimation, nitrate estimation, Total organic carbon estimation were done following Indian standard analytical methods. Glucose consumption efficiency of Lacto bacillus sp.in solid waste substrate was analyzed in UV spectrometer at620nm by Anthrone method. Estimation of lactic acid produced was done in UV spectrometric analysis at 390nm using iron lactate formation. In this study, lactic acid bacteria were isolated from samples of milk, curd, idli batter and screened for lactic acid production. Three isolates were chosen for the study each one from milk, curd, idli batter and from biochemical tests and morphology confirmed as Lacto bacillus sp. Solid waste collected was pretreated with dilute acid heating and hydrolysate obtained was used as substrate for lactic acid production under optimized parameters. Three different substrates were chosen for lactic acid production and the results were compared. The three isolates of Lacto bacillus sp.were good producers of lactic acid and utilized biodegradable solid waste in an effective manner. 3-5 gm/lit of lactic acid was produced by the three strains of Lacto bacillus sp .Segregation of domestic solid waste collected, comprised of 40% biodegradable fragment, 40% nondegradable plastic, 20%miscellaneous waste. Lactic acid production from biodegradable portion is a preliminary step experimented in this study progressing in polylactic acid PLA pathway indirectly reducing plastic pollution. In a similar manner, nonbiodegradable plastic waste can be minimized using Exopolysaccharide pathway by Xanthomonas sp.that is reviewed in this study. Disposal of solid waste in an economical and greener way is a smart method of saving environment. Bioconversion of plastic into pseudoplastic xanthan gum by Xanthomonas sp.(through exopolysaccharide pathway) are greener techniques to crush down the accumulated plastic mountains to ground level.

An Improved Computational Learning-Based Model for Estimating Total Organic Carbon in Unconventional Shale Reservoirs

Unconventional resources have emerged as one of the crucial alternatives to the rapidly depleting of conventional hydrocarbon resources. The hydrocarbon potential of shale source rocks is assessed by the percentage of the organic index such as total organic carbon (TOC). Correct estimation of TOC is very important since minor deviations in anticipated results can lead to wastage of investments and time. A slight improvement in estimation practices, on the other hand, can increase the value of an exploration project. Therefore, the objective of this study is to present an improved classification and regression tree (CART) computational learning-based model as an improved alternative in estimating TOC from well logging data. Conventional well logs suite of bulk density, gamma-ray, deep resistivity, sonic transit time, spontaneous potential, and neutron porosity from Mihambia, Mbuo and, Nondwa, Formations of the Mandawa Basin Tanzania, were used as input variables. Results from the developed CART TOC model were compared with the random forest (RF) and backpropagation neural network (BPNN). It was observed that the proposed CART model trained better while generalizing better through unused testing data compared with RF and BPNN. CART model achieved R, RMSE, and MAPE values of 0.9615, 0.0840, and 0.5035 for training and 0.9703, 0.1162, and 0.3722 for testing respectively. The proposed model work with higher accuracy with the sensitivity analysis indicating that gamma-ray, deep resistivity, and sonic transit time significantly influenced the model outcome.

Total organic carbon estimation in seagrass beds in Tauranga Harbour, New Zealand using multi-sensors imagery and grey wolf optimization

Estimation of carbon stock in seagrass meadows is in challenges of paucity of assessment and low accuracy of the estimates. In this study, we used a fusion of the synthetic aperture radar (SAR) Sentinel-1 (S-1), the multi-spectral Sentinel-2 (S-2), and coupled this with advanced machine learning (ML) models and meta-heuristic optimization to improve the estimation of total organic carbon (TOC) stock in the Zostera muelleri meadows in Tauranga Harbour, New Zealand. Five scenarios containing combinations of data, ML models (Random Forest, Extreme Gradient Boost, Rotation Forest, CatBoost) and optimization were developed and evaluated for TOC retrieval. Results indicate a fusion of S1, S2 images, a novel ML model CatBoost and the grey wolf optimization algorithm (the CB-GWO model) yielded the best prediction of seagrass TOC (R 2, RMSE were 0.738 and 10.64 Mg C ha−1). Our results provide novel ideas of deriving a low-cost, scalable and reliable estimates of seagrass TOC globally.

Effect of lithological variations on the performance of artificial intelligence techniques for estimating total organic carbon through well logs

By the expansion of production from source-related unconventional petroleum resources, accurate approximation of Total Organic Carbon (TOC) through well logs has become progressively important. Accordingly, recent studies have mainly focused on increasing the precision of TOC estimation by using different types of AI techniques and/or optimizing algorithms. Along with utilizing these approaches, this study emphasized on removing an unaddressed source of error inherited from lithological heterogeneity with the same goal. Like organic matter quantity, lithological variations within a source interval also induce well log responses, which may interfere with the training process of Artificial Intelligence (AI) techniques. In the present research, the effect of lithological variations on the performance of TOC estimators was evaluated by employing Adaptive Neuro Fuzzy Inference System (ANFIS) and Multilayer Perceptron network (MLP). Firstly, ANFIS and MLP models were constructed and trained using a database containing different lithologies (original models). Then, a new methodology was developed based on modeling the relationship between log data and TOC values for each type of lithology (litho-based method). The result showed that the litho-based method estimates more reliable and accurate TOC values, proving the adverse effect of lithological variations on the original models. Furthermore, the litho-based ANFIS models provide the most promising results. Since metaheuristic algorithms are usually employed to optimize AI techniques, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were also implemented into the original models (hybrid models). Accuracy of TOC values estimated by the hybrid models is slightly higher than those derived from the original models. However, these hybrid approaches are not as efficient as the litho-based method. Applicability of the proposed approach was guaranteed by performing it over Pabdeh source rocks in a well of SW Iran. Based on its high efficiency, the newly developed litho-based method can be used as a powerful tool to reliably evaluate unconventional hydrocarbon resources, as well as the potential of the conventional petroleum sources. Moreover, it can be utilized, instead of/along with traditional optimization approaches, to approximate other geochemical factors as well as petrophysical parameters from log data.

TOC estimation of shale oil reservoir by combining nuclear magnetic resonance logging and nuclear physics logging

Abstract The evaluation of source rock properties has become a vital step in logging interpretation. Total organic carbon (TOC) content is the key to estimating the quality and hydrocarbon generation potential of source rocks. In the shale oilfield of the Junggar Basin, the conventional method of calculating the TOC of hydrocarbon source rocks cannot satisfy logging evaluation requirements. This paper predominantly deals with a method for the quantitative estimation of TOC in source rocks via nuclear physics and nuclear magnetic resonance (NMR) logs. According to this method, the total hydrogen index of the source rock is the sum of the response of kerogen, clay minerals and fluid, expressed by corrected neutron porosity. The hydrogen index of fluid and clay minerals is indicated by the effective porosity of NMR and the estimated clay content, respectively. To eliminate the hydrogen index of fluid, the effective NMR porosity is subtracted from the corrected neutron porosity. On this basis, a new and overlapping method suitable for clay-rich rocks and oil reservoirs is proposed. This method was developed by overlaying the scaled clay content curve on the hydrogen index curve. In non-source rocks, the two curves regularly overlap. However, in organic-rich rocks the two curves will separate. The separation distance between the two curves was used to estimate TOC continuously. Possessing sound application and benefiting from the measured results of sweet spots, this method provides new insights for TOC quantitative prediction in shale oil reservoirs.

A Bayesian multivariate model using Hamiltonian Monte Carlo inference to estimate total organic carbon content in shale

The prediction of total organic carbon (TOC) content using geophysical logs is one of the key steps in shale reservoir characterization. Various empirical relations have previously been used for the estimation of TOC content from well-logs; however, uncertainty quantification in the model estimation is often ignored while performing TOC estimation in a deterministic framework. We introduce the problem of TOC estimation in a Bayesian setting with the goal of enhancing the TOC content prediction together with the quantification of the uncertainty in the model prediction. To signify the uncertainty, we draw random samples of model parameters from the posterior distribution by realizing multidimensional stochastic processes within the Hamiltonian Monte Carlo algorithm. The posterior model for the variables that influence TOC estimation is conditioned on the available well-log observations and is further defined by a priori and likelihood distributions. We demonstrate examples of applications of this approach to estimate the TOC content on two real field data sets from the well-known Devonian Duvernay shale of Western Canada and the Silurian shale of the Ahnet Basin. The accuracy in the estimation is arbitrated by comparing the prediction results with those obtained using the two most widely used empirical models. Corroborating the results by the laboratory-measured TOC contents demonstrate that the Bayesian approach offers a more reliable and better confidence in predictions when compared with the empirical models, as it provides additional information on the prediction uncertainty. Finally, the implications of the present approach are derived in terms of depositional environments to characterize the high TOC content zone in the studied organic shale formations.

Sunlight mediated photo-Fenton degradation of tetracycline antibiotic and methylene blue dye in aqueous medium using FeWO4/Bi2MoO6 nanocomposite

An optimized batch of 0.25FeWO4/Bi2MoO6 nanocomposites has been developed as an efficient solar photo-Fenton catalyst for degradation of tetracycline antibiotic and methylene blue in an aqueous medium. The catalyst is synthesized by hydrothermal route. Its structure, composition, and morphology are thoroughly characterized. The catalyst dose of 30 mg/50 mL spiked with 20 µL of H2O2 (30% v/v) resulted in 97% degradation of tetracycline and 99% degradation of methylene blue within 90 min. The corresponding degradation rate constants are 0.026 min−1 and 0.043 min−1, respectively. The total organic carbon estimation indicated mineralization of ~80% tetracycline and ~90% methylene blue. The photo-Fenton degradation of tetracycline and methylene blue by FeWO4/Bi2MoO6 nanocomposites is significantly enhanced as compared to Bi2MoO6 photocatalyst, attributed to photo-Fenton heterojunction catalyst, charge carrier separation and mobility, and sustained reactive oxygen species (ROS) generation. The role of ROS, e.g., hydroxyl radicals and superoxide radicals, towards the degradation process is confirmed from ROS scavenging studies. The degradation mechanism has been discussed by identifying the degradation products by ultra-performance liquid chromatography (UPLC)-Q-Tof-MS technique.

Sunlight mediated enhanced photocatalytic degradation of antibiotics in aqueous medium using silicon doped carbon quantum dots decorated Bi2MoO6 nanoflakes

Residual antibiotics in water bodies, including drinking water has led to developing strategies for their removal. Here we report a very efficient method of photocatalytic degradation of tetracycline and ciprofloxacin in an aqueous medium using an optimized batch of silicon doped carbon quantum dot decorated on bismuth molybdate (1.0SiCQDs/Bi2MoO6) nanocomposite as a solar photocatalyst. The photocatalyst is synthesized by the hydrothermal route. Its structural, compositional, morphological, and optical properties are thoroughly characterized. About 97% of tetracycline (TC) and 95% of ciprofloxacin (CIP) are degraded in 90 min of sunlight exposure with a photocatalyst dose of 60 mg per 100 mL, which corresponded to a degradation rate of 0.023 min−1 and 0.025 min−1, respectively. The total organic carbon estimation in the reaction medium after photocatalytic degradation revealed 87.1% and 80.2% mineralization of TC and CIP, respectively. The photocatalytic degradation is attributed to the in-situ generation of reactive oxygen species (ROS), e.g., superoxide radicals, hydroxide radicals, and the role of ROS towards the degradation of tetracycline and ciprofloxacin is confirmed from ROS scavenging studies. The degradation mechanism has been discussed by analyzing the degradation products via ultra-performance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry (UPLC-Q-Tof-MS). The photocatalytic degradation of tetracycline and ciprofloxacin by 1.0SiCQDs/Bi2MoO6 nanocomposites is significantly enhanced as compared to Bi2MoO6 photocatalyst, attributed to interfacial transfer of a photogenerated electron from Bi2MoO6 to SiCQDs, leading to effective charge separation of Bi2MoO6 and the charge carrier mobility.

Ensemble Learning for Predicting TOC from Well-Logs of the Unconventional Goldwyer Shale

Precise estimation of total organic carbon (TOC) is extremely important for the successful characterization of an unconventional shale reservoir. Indirect traditional continuous TOC prediction methods from well-logs fail to provide accurate TOC in complex and heterogeneous shale reservoirs. A workflow is proposed to predict a continuous TOC profile from well-logs through various ensemble learning regression models in the Goldwyer shale formation of the Canning Basin, WA. A total of 283 TOC data points from ten wells is available from the Rock-Eval analysis of the core specimen where each sample point contains three to five petrophysical logs. The core TOC varies largely, ranging from 0.16 wt % to 4.47 wt % with an average of 1.20 wt %. In addition to the conventional MLR method, four supervised machine learning methods, i.e., ANN, RF, SVM, and GB are trained, validated, and tested for continuous TOC prediction using the ensemble learning approach. To ensure robust TOC prediction, an aggregated model predictor is designed by combining the four ensemble-based models. The model achieved estimation accuracy with R2 value of 87%. Careful data preparation and feature selection, reconstruction of corrupted or missing logs, and the ensemble learning implementation and optimization have improved TOC prediction accuracy significantly compared to a single model approach.

Estimation of Total Organic Carbon in Source Rocks by Using Back-propagation Artificial Neural Network and Passay Method-A Case Study

The purpose of this study is to calculate Total Organic Carbon (TOC) values of the Iranian field using a combination of sonic and resistivity logs (Passay method) and neural networks method in the conditions, where the core analysis or well-log measurement does not exist. We compared the resultant TOC with the ones obtained from the geochemical analysis. To correlate between the total organic carbon data and petrophysical log, which are available after logging, Multilayer Perceptron Artificial Neural Network is used. After analyzing 100 cutting samples by using rock -Eval pyrolysis, geochemical parameters have achieved. By using the multi-layer perceptron with Levenberg–Marquardt training algorithm, the TOC with correlation coefficient 0.88 and MSE 1.443 have been provided in the intervals without analyzed samples. Finally, the TOC was estimated by using separation of resistivity and the sonic log, although, with the favorable results in some other fields, the estimation had a correlation coefficient of 51% in this field. Comparing the performance of the multi-layer perceptron with Levenberg–Marquardt training algorithm (with an accuracy of 88%) and results of the Passay method (with an accuracy of 51%) indicated that the neural network is more accurate and has better consistency compared with the empirical formula.

An improved ΔlogR model for evaluating organic matter abundance

Estimating the total organic carbon (TOC) content is crucial in shale reservoir evaluation. Among the available methods for TOC estimation from well logs, the ΔlogR technique is currently the only globally used model that is based on a rigorous petrophysical model. Literature reviews reveal that the overlay-coefficient between the porosity and the resistivity curves in the ΔlogR model has been the focus of many studies. However, the current methods of determining the overlay-coefficient based on the rock minerals or empirical parameters weaken its applicability and accuracy. In this study, we developed a V-ΔlogR model for the prediction of the total organic matter content. The V-ΔlogR model is comprised of three modifications to the ΔlogR model, i.e., replacing the fixed overlay-coefficient with a variable one, removing the maturity, and replacing the baselines with a single one. Among these, a critical improvement is treating the overlay-coefficient ( k ) as a variable and determining it from the measured TOC values or from well logs. The application of our improved model in the Songliao, Bohai Bay, and Sichuan basins indicates that the V-ΔlogR model can accurately predict the TOC of shale with large lithological and mature variations. Because it uses actual geological data to determine the overlay-coefficient in the model, the V-ΔlogR model offers more reliable results for test wells than other commonly used models (e.g., the modified Schomoker, multiple regression analysis, modified ΔlogR). The V-ΔlogR model eliminates the need for mineral and maturity data constraints and the overlay-coefficient can be determined using only the well logs, which significantly expands the applicability of the ΔlogR method. • An improved method (the V-ΔlogR model) predicts TOC content is proposed. • The method is based on sonic and the resistivity log, and a ratio of k between them. • A easily implemented method for predicting k using the log curves is presented. • The V-ΔlogR model expands the applicability of the ΔlogR method, and can be accurate in TOC predicting.

Applying artificial intelligent and empirical techniques for prediction of total organic carbon content from wireline data: A case study from Pabdeh Formation in Mansuri oilfield, SW Iran

Total organic carbon (TOC) content is one of the most crucial parameters needed for petroleum source rock evaluation. In this study, artificial neural network (ANN) and empirically derived ΔlogR techniques were used to estimate organic richness of the Paleocene-Oligocene Pabdeh source rock in the Mansuri oilfield (SW Iran) using the wireline data. The ANN model trained by the Levenberg-Marquardt back propagation algorithm was employed for prediction of TOC content from the responses of the sonic, neutron, density and spectral gamma-ray well logs. The linear regression analysis between TOC values measured by Rock-Eval analysis and the corresponding ones predicted by ΔlogR and ANN methods was performed to validate the performance of each method. The ANN model is capable of predicting TOC contents with higher coefficient of determination (R2) value (0.7830) compared to ΔlogR technique (0.5091). Correspondingly, the mean squared error (MSE) value for the constructed neural network (0.1647) is lower than ΔlogR method (0.2410). Overall, it can be concluded that the proposed ANN model provides more accurate qualitative and quantitative predictions of organic richness, and can be reliably applied for high-resolution estimation of TOC contained in the Pabdeh source rock throughout the Mansuri oilfield.

Effective machine learning identification of TOC-rich zones in the Eagle Ford Shale

Successful hydrocarbon production in the Eagle Ford relies on technological advances such as directional geosteering, horizontal drilling, and hydraulic fracturing, as well as the identification and delineation of organic-rich intervals and fracture zones. Total organic carbon (TOC) is an excellent indicator for the organic richness and the hydrocarbon potential of shale in unconventional reservoirs; however, common methods for TOC estimation have many underlying assumptions and rely on empirical formulas. It is useful to develop a robust data-driven approach that could be used to reliably identify TOC-rich zones in unconventional plays such as the Eagle Ford. Using gamma-ray, deep resistivity, and sonic wireline well log data from La Salle County, South Texas, we generate a layer unit database and label the layer units using core measured TOC values. We apply a data-driven binary support vector machine (SVM) machine learning approach to identify TOC-rich zones within the Eagle Ford. We evaluate the performance of the SVM classifier and obtain F1 scores ranging from 97.4% to 99.4%. The methodology successfully identifies TOC-rich zones that match with geological observations, the ΔlogR method, and independently obtained core TOC measurements. We demonstrate the successful cross-well application of the methodology within the Eagle Ford Play Area and also demonstrate the successful cross-basin application of the methodology using data from the Barnett Shale Formation, Fort Worth Basin, North Texas, and the Duvernay Shale Formation of the West Canadian Sedimentary Basin, Canada.

Brittleness computation from rock moduli without density

Brittleness is a key property for horizontal well placement, fracture development, and production sustainability assessments of unconventional shale and gas plays. Conventionally, brittleness is estimated from Young’s modulus and Poisson’s ratio, and averaged. As Young’s modulus is a function of density, field wide brittleness estimation from Young’s modulus after seismic inversion may be compromised because density is the least accurate estimate compared to acoustic impedance and shear impedance. A simple quantitatively equivalent brittleness computation can be made from using Young’s modulus × density rather than directly from Young’s modulus. Further, it is preferable to use the average brittleness estimation from Young’s modulus × density and shear modulus × density rather than the conventional average, because Poisson’s ratio is an indicator of lithology rather than brittleness. Examples of brittleness and total organic carbon estimation first from well logs, then from rock parameters inverted from 3D land seismic data successfully mapped a brittle shale zone in a Silurian hot shale, a known source rock onshore Turkey and now targeted as oil shale play. The estimated brittleness and total organic carbon showed agreement with the core analysis made at another well on the same prospect.

Improved total organic carbon convolutional neural network model based on mineralogy and geophysical well log data

Abstract Unconventional resources, such as shale oil and gas, are currently regarded as an essential resource in the face of depleting conventional hydrocarbon reserves. In line with this, the accurate determination of the hydrocarbon potential of a shale reservoir is critical and relies in part, on the total organic carbon (TOC) content. However, there exist challenges when determining TOC by conducting geochemical analysis of rock samples or developing a relationship between well log response and TOC values. Variations in the mineral composition and TOC values can influence the performance of well log-based models. There are few applications integrating mineral composition and well log response in an artificial intelligence (AI) model for TOC estimation. This study incorporates the mineral composition of the shale and well log data in developing a deep convolutional neural network (CNN) model for predicting TOC. The statistical results of the study have shown that the proposed mineralogy and well log-based CNN (MWL-CNN) outperformed well log-based CNN (WL-CNN). The sensitivity analysis performed indicate that the mineral constituents that significantly influenced the model outcome were feldspar and pyrite. These findings have established that mineral composition has a great effect on TOC predictions and must be incorporated in the model for a more accurate result.

Hierarchical copper oxide as efficient enzymeless amperometric biosensor and promising photocatalyst

Thin films of hierarchical CuO nanostructures have been prepared. The synthesized material was thoroughly characterized with the help of advanced instrumentation techniques. The CuO modified FTO electrode, i.e. FTO/CuO was taken for amperometric glucose sensing as enzymatic mimic that came out with excellent result with a notably low detection limit of 0.0084 mM. The notably high optical absorption in visible region also makes the material a good candidate to capture visible photons and facilitate photocatalysis of some non-biodegradable organic chemicals like Rhodamine B, Methylene Blue and Congo Red with a faster and better kinetics than many previous reports. The hierarchical structure allows good interaction between the greatly exposed surfaces and facilitates the amperometric sensing and photochemical performances. The estimation of total organic carbon (TOC) content also strengthens the photocatalytic attributes of the synthesized material.