A deep-learning approach for reservoir evaluation for shale gas wells with complex fracture networks

The complex fracture networks in shale gas reservoirs bring greater challenges and uncertainties to the modeling in reservoir evaluation. As the  emerging potential technology, deep learning can be usefully applied to many aspects of reservoir evaluation. To further conduct the reservoir evaluation in rate transient analysis, this work proposes a data-driven proxy model for accurately evaluating the horizontal wells with complex fracture networks in shales. The production time, variable bottom hole pressure, and the fracture networks properties are used as input variables, while the output variable refers to the production for the forecast time period. The data from boundary element method is used to generate the proxy model for the learning process. The method of shuffled cross-validation is used to increase the model’s accuracy and generalizability. The proxy model is coupled with recently developed deep learning methods such as attention mechanism, skip connection, and cross-validation to address the time series analysis problem for multivariate operating and physical parameters. Results demonstrate that the attention mechanism is robust. The operating parameters analysis shows that the attention mechanism has the ability to analyze variable pressure drop/flowrate data. Sensitivity analysis also indicates that the model takes into account the geometric characteristics of fracture network. The model reliability is proved by a case study from Marcellus shale. The computation time of the trained attention mechanism model is approximately 0.3 s, which equates to 3.8% of the physical model’s running time.Cited as: Chu, H., Dong, P., Lee, W. J. A deep-learning approach for reservoir evaluation for shale gas wells with complex fracture networks. Advances in Geo-Energy Research, 2023, 7(1): 49-65. https://doi.org/10.46690/ager.2023.01.0

An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

A new semi-analytical flow model for multi-branch well testing in natural gas hydrates

This paper presents a new semi-analytical solution and the related methodology to analyze the pressure behavior of multi-branch wells produced from natural gas hydrates. For constant bottom-hole pressure production, the transient flow solution is obtained by Laplace transforms. The interference among various branches is investigated using the superposition principle. A simplified form of the proposed model is validated using published analytical solutions. The complete flow profile can be divided into nine distinct regimes: wellbore storage and skin, vertical radial flow, linear flow, pseudo-radial flow, composite flow, dissociated flow, transitional flow, improvement flow and stress-sensitive flow. A well’s multi-branch structure governs the vertical radial and the linear flow regimes. In our model, a dynamic interface divides the natural gas hydrates deposit into dissociated and non-dissociated regions. Natural gas hydrates formation properties govern the compositeeffect, dissociated, transitional, and improvement flow regimes. A dissociation coefficient governs the difference in flow resistance between dissociated and non-dissociated natural gas hydrates regions. The dissociated-zone radius affects the timing of these flow regimes. Conversion of natural gas hydrates to natural gas becomes instantaneous as the dissociation coefficient increases. The pressure derivative exhibits the same features as a homogeneous formation. The natural gas hydrates parameter values in the Shenhu area of the South China Sea cause the prominent dissociated flow regime to conceal the later transitional and improvement flow regimes. Due to the maximum practical well-test duration limitation, the first five flow regimes (through composite flow) are more likely to appear in practice than later flow regimes.Cited as: Chu, H., Zhang, J., Zhang, L., Ma, T, Gao Y., Lee, W. J. A new semi-analytical flow model for multi-branch well testing in natural gas hydrates. Advances in Geo-Energy Research, 2023, 7(3): 176-188. https://doi.org/10.46690/ager.2023.03.0

A Semi-Tensor Product based Circuit Simulation for SAT-sweeping

In recent years, circuit simulators and Boolean satisfiability (SAT) solvers have been tightly integrated to provide efficient logic synthesis and verification. Circuit simulation can generate highly expressive simulation patterns that can either enumerate or filter out most candidates for synthesis. Subsequently, SAT solvers are employed to check those that remain, thereby making the logic synthesis process more efficient. This paper introduces a novel circuit simulator of k-input lookup table (k-LUT) networks, based on semi-tensor product (STP). STP-based simulators use computation of logic matrices, the primitives of logic networks, as opposed to relying on bitwise logic operations for simulation of k-LUT networks. Experimental results show that our STP-based simulator reduces the runtime by an average of 7.2x. Furthermore, we integrate this proposed simulator into a SAT-sweeping engine known as SAT sweeper. Through a combination of structural hashing, simulation, and SAT queries, SAT sweeper simplifies logic networks by systematically merging graph vertices from input to output. To enhance the efficiency, we used STP-based exhaustive simulation, which significantly reduces the number of false equivalence class candidates, thereby improving the computational efficiency by reducing the number of SAT calls required. When compared to the SOTA SAT sweeper, our method demonstrates an average 35% runtime reduction.Comment: In this 6 page manuscript, we introduce a novel Semi-Tensor Product based circuit simulation for SAT-sweeping in DATE'2

Protective Effect Against Toxoplasmosis in BALB/c Mice Vaccinated With Toxoplasma gondii Macrophage Migration Inhibitory Factor

Toxoplasma gondii is an obligate intracellular parasite responsible for toxoplasmosis, which can cause severe disease in the fetus and immunocompromised individuals. Developing an effective vaccine is crucial to control this disease. Macrophage migration inhibitory factor (MIF) has gained substantial attention as a pivotal upstream cytokine to mediate innate and adaptive immune responses. Homologs of MIF have been discovered in many parasitic species, and one homolog of MIF has been isolated from the parasite Toxoplasma gondii. In this study, the recombinant Toxoplasma gondii MIF (rTgMIF) as a protein vaccine was expressed and evaluated by intramuscular injection in BALB/c mice. We divided the mice into different dose groups of vaccines, and all immunizations with purified rTgMIF protein were performed at 0, 2, and 4 weeks. The protective efficacy of vaccination was analyzed by antibody assays, cytokine measurements and lymphoproliferative assays, respectively. The results obtained indicated that the rTgMIF vaccine elicited strong humoral and cellular immune responses with high levels of IgG antibody and IFN-γ production compared to those of the controls, in addition to slight higher levels of IL-4 production. After vaccination, a stronger lymphoproliferative response was also noted. Additionally, the survival time of mice immunized with rTgMIF was longer than that of the mice in control groups after challenge infection with virulent T. gondii RH tachyzoites. Moreover, the number of brain tissue cysts in vaccinated mice was reduced by 62.26% compared with the control group. These findings demonstrated that recombinant TgMIF protein is a potential candidate for vaccine development against toxoplasmosis

GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Well Testing Methodology for Multiple Vertical Wells with Well Interference and Radially Composite Structure during Underground Gas Storage

To achieve the goal of decarbonized energy and greenhouse gas reduction, underground gas storage (UGS) has proven to be an important source for energy storage and regulation of natural gas supply. The special working conditions in UGS cause offset vertical wells to easily interfere with target vertical wells. The current well testing methodology assumes that there is only one well, and the interference from offset wells is ignored. This paper proposes a solution and analysis method for the interference from adjacent vertical wells to target vertical wells by analytical theory. The model solution is obtained by the solution with a constant rate and the Laplace transform method. The pressure superposition is used to deal with the interference from adjacent vertical wells. The model reliability in the gas injection and production stages is verified by commercial software. Pressure analysis shows that the heterogeneity and interference in the gas storage are caused by long-term gas injection and production. As both the adjacent well and the target well are in the gas production stage, the pressure derivative value in radial flow is related to production rate, mobility ratio, and 0.5. Gas injection from offset wells will cause the pressure derivative to drop later. Multiple vertical wells from the Hutubi UGS are used to illustrate the properties of vertical wells and the formation