Search Results (6)

Accurate Total Organic Carbon (TOC) prediction in the deeply buried Lower Cambrian Qiongzhusi Formation shale is constrained by extreme heterogeneity (TOC variability: 0.5–12 wt.%, mineral composition Coefficient of Variation > 40%) and ambiguous geophysical responses. This study introduces three key innovations to address these challenges: (1) A Dynamic Weighting–Calibrated Random Forest Regression (DW-RFR) model integrating high-resolution Gamma-Ray-guided dynamic time warping (±0.06 m depth alignment precision derived from 237 core-log calibration points using cross-validation), Principal Component Analysis-Deyang–Anyue Rift Trough Shapley Additive Explanations (PCA-SHAP) hybrid feature engineering (89.3% cumulative variance, VIF < 4), and Bayesian-optimized ensemble learning; (2) systematic benchmarking against conventional ΔlogR (R² = 0.700, RMSE = 0.264) and multi-attribute joint inversion (R² = 0.734, RMSE = 0.213) methods, demonstrating superior accuracy (R² = 0.917, RMSE = 0.171); (3) identification of Gamma Ray (r = 0.82) and bulk density (r = −0.76) as principal TOC predictors, contrasted with resistivity’s thermal maturity-dependent signal attenuation (r = 0.32 at Ro > 3.0%). The methodology establishes a transferable framework for organic-rich shale evaluation, directly applicable to the Longmaxi Formation and global Precambrian–Cambrian transition sequences. Future directions emphasize real-time drilling data integration and quantum computing-enhanced modeling for ultra-deep shale systems, advancing predictive capabilities in tectonically complex basins. Full article

Global recoverable shale gas reserves are estimated to be 214.5 × 10¹² m³. Estimation methods for shale gas resources, such as volumetric, analog, and genetic approaches, have been widely used in previous studies. However, these approaches have notable limitations, including the substantial effect of rock heterogeneity, difficulties in determining the similarity of analog accumulations, and unsuitability for evaluating high-mature–overmature source rocks. In the Qiongzhusi Formation (Є1q) of the Sichuan Basin, China, extensive development of high-mature–overmature shales has led to significant advancements in conventional and unconventional shale gas exploration. This progress highlights the need for the development of an integrated evaluation system for conventional and unconventional resources. Hence, this study uses the whole petroleum system theory and an improved hydrocarbon generation potential method to analyze the distribution patterns of hydrocarbon generation, retention, and expulsion during various stages of oil and gas accumulation in the Є1q. In addition, it assesses the resource potential of conventional and shale oil and gas. Hydrocarbon generation and expulsion centers are favorable exploration targets for conventional oil and gas, primarily located in the central and northern regions of the Mianyang—Changning rift trough, with an estimated resource potential of 6560 × 10¹² m³. Hydrocarbon retention centers represent promising targets for shale oil and gas exploration, concentrated in the central Mianyang—Changning rift trough, with a resource potential of 287 × 10¹² m³. This study provides strategic guidance for future oil and gas exploration in the Є1q and offers a methodological reference for integrated resource assessments of conventional and unconventional oil and gas systems of high-mature–overmature source rocks in similar basins worldwide. Full article

Background: As the anti-biofilm pharmacokinetic/pharmacodynamic (PK/PD) properties of antibiotics are not well-defined, we have evaluated the PK/PD indices for different regimens of ceftazidime (CAZ; with/without colistin) against Pseudomonas aeruginosa biofilm. Methods: We have used the Center for Disease Control and Prevention Biofilm Reactor with two susceptible (PAO1 and HUB-PAS) and one resistant (HUB-XDR) strains of P. aeruginosa. The regimens were CAZ monotherapies (mimicking a human dose of 2 g/8 h, CAZ-IB; 6 g/daily as continuous infusion at 50 mg/L, CAZ-CI₅₀; and 9 g/daily at 70 mg/L, CAZ-CI₇₀) and CAZ-colistin combinations. Efficacy was correlated with the CAZ PK/PD parameters. Results: CAZ-CI₇₀ was the most effective monotherapy against CAZ-susceptible strains (Δlog CFU/mL 54–0 h = −4.15 ± 0.59 and −3.05 ± 0.5 for HUB-PAS and PAO1, respectively; p ≤ 0.007 vs. other monotherapies), and adding colistin improved the efficacy over CAZ monotherapy. CAZ monotherapies were ineffective against the HUB-XDR strain, and CAZ-CI₅₀ plus colistin achieved higher efficacy than CAZ-IB with colistin. The PK/PD index that correlated best with anti-biofilm efficacy was fAUC_0–24h/MIC (r² = 0.78). Conclusions: CAZ exhibited dose-dependent anti-biofilm killing against P. aeruginosa, which was better explained by the fAUC_0–24h/MIC index. CAZ-CI provided benefits compared to CAZ-IB, particularly when using higher doses and together with colistin. CAZ monotherapies were ineffective against the CAZ-resistant strain, independently of the optimized strategy and only CAZ-CI plus colistin appeared useful for clinical practice. Full article

Total organic carbon (TOC) content is a key indicator for determining the hydrocarbon content of shale. The current model for calculating the TOC content of shale is relatively simplistic, the modeling process is cumbersome, and the parameters involved are influenced by subjective factors, which have certain shortcomings. To address this problem, a time-domain convolutional neural network (TCN) model for predicting total organic carbon content based on logging sequence information was established by starting from logging sequence information, conducting logging parameter sensitivity analysis experiments, prioritizing logging-sensitive parameters as model feature vectors, and constructing a TCN network. Meanwhile, to overcome the problem of an insufficient sample size, a five-fold cross-validation method was used to train the TCN model and obtain the weight matrix with the minimum error, and then a shale reservoir TOC content prediction model based on the TCN model was established. The model was applied to evaluate the TOC logging of the Lianggaoshan Formation in the Sichuan Basin, China, and the predicted results were compared with the traditional ΔlogR model. The results indicate that the TCN model predicts the TOC content more accurately than the traditional model, as demonstrated by laboratory tests. This leads to a better application effect. Additionally, the model fully explores the relationship between the logging curve and the total organic carbon content, resulting in improved accuracy of the shale TOC logging evaluation. Full article

To accurately evaluate the shale oil resources in the Funing Formation of the Gaoyou Sag, Subei Basin, light and heavy hydrocarbon correction models of S₁ were developed based on the rock pyrolysis of liquefrozen, conventional, and oil-washed shales. The improved ΔlogR technique was applied to establish the TOC, S₁, and S₂ logging evaluation methods. The results showed that the S₂ values after oil washing were significantly lower than before. The difference between these two S₂ (ΔS₂) values is the heavy hydrocarbon correction amount of S₁, which is about 0.69 S₂. There was almost no loss of light hydrocarbons during liquefrozen shales’ pyrolysis tests; the ratio of liquefrozen to conventional S₁ values is the light hydrocarbon correction factor, which is about 1.67. The corrected S₁ is about 3.2 times greater than the conventional shale-tested value. The S₁ and TOC are obviously “trichotomous”; a TOC greater than 1.5% and corrected S₁ larger than 4.0 mg/g corresponds to the enriched resource. The logging estimated results show that the total shale oil resources in the E₁f₂ of the Gaoyou Sag are about 572 million tons, of which the enriched resource is about 170 million tons. Full article

Source rock evaluation plays a key role in studies of hydrocarbon accumulation and resource potential. Total organic carbon (TOC) is the basis of source rock evaluation and it is a key parameter that influences petroleum resource assessment. The Majiagou formation in the eastern Ordos Basin has complicated lithology and low abundance of organic matters. There are different opinions over the existence of scale source rocks. Due to inadequate laboratory data of TOC in the Ordos Basin, it is difficult to accurately describe source rocks in the region; thus, log interpretation of TOC is needed. In this study, the neural network model in the artificial intelligence (AI) field was introduced into the TOC logging interpretation. Compared with traditional ΔlogR methods, sample optimization, logging correlation analysis and comparative optimization of computational methods were carried out successively by using measured TOC data and logging data. Results show that the neural network model has good prediction effect in complicated lithologic regions and it can identify variations of TOC in continuous strata accurately regardless of the quick lithologic changes. Full article