In the Perm region, the south and the central part have been exhaustively studied and as a result, it is here that most of the oil and gas fields have been discovered and developed, while less attention has been paid to the north of the region. The article briefly examines the history of the sedimentary cover in the northern Perm region, and evaluates the prospects for oil and gas potential based on the ranking of objects using the de Borda method. The parameters reflecting the conditions of hydrocarbon generation and accumulation are chosen as the basis for the ranking. As a result of this work, a map of the most promising areas for oil and gas exploration has been compiled.
$\text{CO}_{2}$ flooding is central to carbon utilization technologies, yet conventional waterflooding models fail to capture the complex interactions between CO$_2$ and formation fluids. In this study, one- and two-dimensional nuclear magnetic resonance experiments reveal that $\text{CO}_{2}$ markedly enhances crude oil mobility during miscible displacement via multiple synergistic mechanisms, yielding a recovery factor of $60.97\%$, which surpasses that of immiscible displacement (maximum $57.53\%$). Guided by these findings, we propose a convection-diffusion model that incorporates the diffusion coefficient ($D$) and porosity ($φ$) as key parameters. This model captures the spatiotemporal evolution of the $\text{CO}_{2}$ front and addresses a key limitation of conventional formulations-the omission of diffusion effects. It improves predictions of gas breakthrough time and enables optimized injection design for low-permeability reservoirs. Extending classical material balance theory, we develop an enhanced $\text{CO}_{2}$ flooding equation that integrates critical transport phenomena. This formulation incorporates $\text{CO}_{2}$ diffusion, oil phase expansion, reservoir adsorption, and gas compressibility to describe the dynamic transport and mass compensation of injected $\text{CO}_{2}$. Validation through experimental and numerical data confirms the model's robustness and applicability under low-permeability conditions. The proposed framework overcomes limitations of physical experiments under extreme environments and offers theoretical insight into oil recovery enhancement and $\text{CO}_{2}$ injection strategy optimization.
Rate of penetration(ROP)is an important indicator to evaluate the petroleum drilling performance.To accurately predict the ROP at an oilfield in the Xinjiang work area,the historical drilling data from the area were processed using the local outlier factor(LOF)algorithm,and an ROP prediction model based on Stacking ensemble learning was established.The model integrated by Stacking strategy with the K-nearest neighbor(KNN),support vector machine(SVM)or random forest(RF)algorithm showed inaccurate classification in the verification.The genetic algorithm was then adopted to optimize the parameters of the basic models.The optimized models integrating KNN,SVM,RF and Stacking algorithms yielded the prediction results with accuracy of 73.7%,78.9%,81.6%,and 97.4%,respectively.Clearly,the Stacking-based model gets the highest accuracy.Thus,a software was developed using the Stacking-based model.It was applied to predict the ROP under two sets of parameters.The results show that the predicted ROP matches well with the actual ROP,and the software works stable.This proves the generalization and accuracy of the Stacking-based model.This intelligent algorithm has provided a new means to predict ROP and optimize drilling parameters at the oilfield of Xinjing work area.
Chemical engineering, Petroleum refining. Petroleum products
Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.
The gas-water distribution and production heterogeneity of tight gas reservoirs have been summarized from experimental and geological observations, but the charging and accumulation mechanisms have not been examined quantitatively by mathematical model. The tight gas charging and accumulation mechanisms were revealed from a combination of physical simulation of nuclear magnetic resonance coupling displacement, numerical simulation considering material and mechanical equilibria, as well as actual geological observation. The results show that gas migrates into tight rocks to preferentially form a gas saturation stabilization zone near the source-reservoir interface. When the gas source is insufficient, gas saturation reduction zone and uncharged zone are formed in sequence from the source-reservoir interface. The better the source rock conditions with more gas expulsion volume and higher overpressure, the thicker the gas saturation stabilization and reduction zones, and the higher the overall gas saturation. When the source rock conditions are limited, the better the tight reservoir conditions with higher porosity and permeability as well as larger pore throat, the thinner the gas saturation stabilization and reduction zones, but the gas saturation is high. The sweet spot of tight gas is developed in the high-quality reservoir near the source rock, which often corresponds to the gas saturation stabilization zone. The numerical simulation results by mathematical model agree well with the physical simulation results by nuclear magnetic resonance coupling displacement, and reasonably explain the gas-water distribution and production pattern of deep reservoirs in the Xujiaweizi fault depression of the Songliao Basin and tight gas reservoirs in the Linxing–Huangfu area of the Ordos Basin.
Production prediction is crucial for the recovery of hydrocarbon resources. However, accurate and rapid production forecasting remains challenging for unconventional reservoirs due to the complexity of the percolation process and the scarcity of available data. To address this problem, a novel model combining a long short-term memory network (LSTM) and support vector regression (SVR) was proposed to forecast tight oil production. Three variables, the tubing head pressure, nozzle size, and water rate were utilized as the inputs of the presented machine-learning workflow to account for the influence of operational parameters. The time-series response of tight oil production was the output and was predicted by the optimized LSTM model. An SVR-based residual correction model was constructed and embedded with LSTM to increase the prediction accuracy. Case studies were carried out to verify the feasibility of the proposed method using data from two wells in the Ma-18 block of the Xinjiang oilfield. Decline curve analysis (DCA) methods, LSTM and artificial neural network (ANN) models were also applied in this study and compared with the LSTM-SVR model to prove its superiority. It was demonstrated that introducing residual correction with the newly proposed LSTM-SVR model can effectively improve prediction performance. The LSTM-SVR model of Well A produced the lowest prediction root mean square error (RMSE) of 5.42, while the RMSE of Arps, PLE Duong, ANN, and LSTM were 5.84, 6.65, 5.85, 8.16, and 7.70, respectively. The RMSE of Well B of LSTM-SVR model is 0.94, while the RMSE of ANN, and LSTM were 1.48, and 2.32.
Petroleum refining. Petroleum products, Engineering geology. Rock mechanics. Soil mechanics. Underground construction
The current status of `fat-brane' minimal Universal Extra Dimensions (fat-mUED) is studied in the light of ATLAS experiment's recent reports. At the Large Hadron Collider (LHC) color charged first level Kaluza-Klein (KK) particles (first level excited quarks and gluons) can be abundantly pair-produced due to conserved quantity, viz., KK-parity, and strong interaction. The cascade decay of these particles to one or more Standard Model (SM) particle(s) and lighter first level KK particle(s) stops after producing the lightest excited massive state, named as the lightest KK particle (LKP). With the presence of gravity induced decays, stability of the LKP is lost and it may decay to photon or Z-boson by radiating KK-excited gravitons, hence leading to final state with photon(s) at the LHC. A variant signal topology is established when pair-produced first level colored KK particles undergo direct decay to an associated SM partner along with KK-excitations of graviton; thus leading to a signal with two hard jets and substantial missing energy. The ATLAS experiment lately reported two searches at 13 TeV LHC with 139 inverse-femtobarn of data; (i) multi-jet and (ii) photon and jets with missing energy. In both searches, the results showed no substantial deviation from the number of background events of the SM. Provided the absence of any number of excess events in both searches we constrained the parameters of the fat-mUED model, viz., the higher-dimensional Planck mass and the compactification scale.
Advancements in monaural speech enhancement (SE) techniques have greatly improved the perceptual quality of speech. However, integrating these techniques into automatic speech recognition (ASR) systems has not yielded the expected performance gains, primarily due to the introduction of distortions during the SE process. In this paper, we propose a novel approach called FAT-HuBERT, which leverages distortion-invariant self-supervised learning (SSL) to enhance the robustness of ASR. To address the distortions introduced by the SE frontends, we introduce layer-wise fusion modules that incorporate features extracted from both observed noisy signals and enhanced signals. During training, the SE frontend is randomly selected from a pool of models. We evaluate the performance of FAT-HuBERT on simulated noisy speech generated from LibriSpeech as well as real-world noisy speech from the CHiME-4 1-channel dataset. The experimental results demonstrate a significant relative reduction in word error rate (WER).
Taking into account new data on features of the geological structure of petroleum bearing areas and oil and gas promising strata of the Ustyurt petroleum region, the results of constructing structural maps are presented. They were the basis for the development of a structural geological model of the Shege field by identifying and tracing tectonic faults, zones of local subsidence and uplifts that control oil and gas manifestations and hydrocarbon accumulations in the Jurassic strata.
The upper Paleozoic Marine shale in middle Guangxi Depression, namely Guizhong Depression, has experienced complex tectonic evolution and thermal evolution. As the main production layer of shale gas, the microscopic pore structure characterization and reservoir pore evaluation of the shale in lower Carboniferous need to be studied urgently. Focus on the Lower Carboniferous Luzhai Formation shale reservoir in the northern Guizhong Depression, the material composition and reservoir pores of the shale are characterized and evaluated in detail by rock thin section, scanning electron microscope, Xray diffraction, porosity and isothermal adsorption tests on samples both from fields and cores. The results show that the <i>TOC</i> in the shale of Luzhai Formation is 0.4 % ~ 6.6 %. The organic matter is in the stage of high mature to over-mature thermal evolution. The content of brittle minerals such as quartz is high, with a good fracturing ability. The shale in Luzhai Formation, with an average porosity of 2.91 % and an average permeability of 0.007 9 ×10<sup>-3</sup>μm<sup>2</sup>, is a kind of low porosity, ultra-low permeability and good breakthrough pressure shale gas reservoir. There are five types of pores in the shale reservoir: the residual intergranular pore, intergranular pore, intragranular dissolved pore, clay minerals interlayer pore and organic pore. The main contributors are the clay minerals interlayer pores, the organic pores and the pyrite intergranular pores. The aperture rangs from 17 nm to 65 nm, most of which are microporous or mesoporous with the scale less than 50 nm. The connectivity between the pores is poor and there is a certain connectivity inside the pores.
Petroleum refining. Petroleum products, Gas industry
This paper considers the operation of offshore oil and gas platform energy systems with energy supply from wind turbines to reduce local CO2 emissions. A new integrated energy system model for operational planning and simulation has been developed and implemented in an open-source software tool (Oogeso). This model and tool is first presented, and then applied on a relevant North Sea case with different energy supply alternatives to quantify and compare CO2 emission reductions and other key indicators.
Rojalin Padhan, Manimala Mitra, Suchita Kulkarni
et al.
We investigate the pair-production of Right-Handed Neutrinos (RHNs) via a $B-L$ $Z'$ boson and the detection prospects at the High-Luminosity run of the LHC (HL-LHC) and a future $pp$ collider (FCC-hh). We focus on RHN states with a mass of $10-70$ GeV which naturally results in displaced vertices for small active-sterile mixing strengths. Being produced through a mass resonance with $m_{Z'} \ge 1$ TeV, the RHNs are heavily boosted, leading to collimated decay products that give rise to fat-jets. We investigate the detection prospect of dedicated signatures in the inner detector and the muon spectrometer, namely a pair of displaced fat-jets and the associated tracks, respectively. We find that both the HL-LHC and FCC-hh can be sensitive to small active-sterile mixing $V_{μN} > 10^{-6}$ and $V_{μN} > 10^{-7}$ with the number of events reaching $\mathcal{O}(10)$ and $\mathcal{O}(10^3)$, respectively. This allows probing the generation of light neutrino masses through the Seesaw mechanism in this scenario.
We propose a new method for construction of the absolute permeability map consistent with the interpreted results of well logging and well test measurements in oil reservoirs. Nadaraya-Watson kernel regression is used to approximate two-dimensional spatial distribution of the rock permeability. Parameters of the kernel regression are tuned by solving the optimization problem in which, for each well placed in an oil reservoir, we minimize the difference between the actual and predicted values of (i) absolute permeability at the well location (from well logging); (ii) absolute integral permeability of the domain around the well and (iii) skin factor (from well tests). Inverse problem is solved via multiple solutions to forward problems, in which we estimate the integral permeability of reservoir surrounding a well and the skin factor by the surrogate model. The last one is developed using an artificial neural network trained on the physics-based synthetic dataset generated using the procedure comprising the numerical simulation of bottomhole pressure decline curve in reservoir simulator followed by its interpretation using a semi-analytical reservoir model. The developed method for reservoir permeability map construction is applied to the available reservoir model (Egg Model) with highly heterogeneous permeability distribution due to the presence of highly-permeable channels. We showed that the constructed permeability map is hydrodynamically similar to the original one. Numerical simulations of production in the reservoir with constructed and original permeability maps are quantitatively similar in terms of the pore pressure and fluid saturations distribution at the end of the simulation period. Moreover, we obtained an good match between the obtained results of numerical simulations in terms of the flow rates and total volumes of produced oil, water and injected water.
In the existing literature of the Mutual Visibility problem for autonomous robot swarms, the adopted visibility models have some idealistic assumptions that are not consistent with practical sensing device implementations. This paper investigates the problem in the more realistic visibility model called opaque fat robots with slim omnidirectional camera. The robots are modeled as unit disks, each having an omnidirectional camera represented as a disk of smaller size. We assume that the robots have compasses that allow agreement in the direction and orientation of both axes of their local coordinate systems. The robots are equipped with visible lights which serve as a medium of communication and also as a form of memory. We present a distributed algorithm for the Mutual Visibility problem which is provably correct in the semi-synchronous setting. Our algorithm also provides a solution for Leader Election which we use as a subroutine in our main algorithm. Although Leader Election is trivial with two axis agreement in the full visibility model, it is challenging in our case and is of independent interest.