Journal of Petroleum Science and Engineering最新文献

This work is an analysis of the flow model often occurs in crude oil extraction, blood flow in the arteries, filtration of underground different fluids flowing together. In this analysis, a model of three-layered porous horizontal channel is considered. This problem is significant because of the different permeability functions used for each porous layer of the channel and flow of different Newtonian fluids takes place in these porous regions. The problem is solved for the general case which can be reduced into several particular cases. The flows inside the channel and in every porous layer is governed by the Brinkman's momentum equation for the porous medium. The momentum equations in each region of the present model are the Airy's inhomogeneous differential equations respectively. An analytical closed form solution of the flow in each region have been obtained. Authors have discussed various results of velocity profile, flow rate and wall shear stresses graphically. Our results agree with the previous published results.

Given that the interfacial tension (IFT) of the CO₂/brine system is a critical parameter in geological CO₂ storage (GCS), this paper presents a systematic review of the present studies on the IFT of CO₂/brine system, mainly including experimental methods, data and influencing factors.

IFT is caused by an imbalance of molecular forces at the interface of two immiscible fluids, and the pendant drop method is considered the most convenient method to determine its value. The axisymmetric drop shape analysis (ADSA) theory derived from the Young-Laplace equation is regarded as the most advanced and accurate theory for the analysis of the results of the pendant drop measurements. The IFT of the CO₂/brine system is determined by the temperature, pressure, salt species and salinity of the brine and the impurity of the CO₂ phase. The results indicated that the greater solubility of CO₂ caused by increasing temperature increase the density difference between brine and CO₂, leading to an increase in the IFT. Under low-pressure conditions, the IFT decreases suddenly due to the increase in the CO₂ solubility and the CO₂ density with pressure. However, after reaching the supercritical state; CO₂ solubility and density hardly change with pressure, thus, IFT does not depend on the pressure. When ions are dissolved in water, the combined effect of decreased solubility of CO₂ in the aqueous phase, and enhanced electrostatic force and interfacial ionic strength gradient leads to a positive effect of the ion concentration on the IFT. The introduction of impurity gases lighter than CO₂ can enhance the IFT by increasing the density difference between CO₂ and brine. The findings of this study can help to better understand the IFT of CO₂/brine systems used for geological CO₂ storage.

Occupational risk evaluation is one of the most indispensable issues in the risk prevention and control process for the natural gas pipeline construction (NGPC) project. The Fine-Kinney model, recognized as an effective occupational risk evaluation technique, has limited capability to handle the occupational risk analysis problem under the complex spherical fuzzy (CSF) environment. Accordingly, a synthetical Fine-Kinney framework based on the compromise ranking of alternatives from distance to ideal solution (CRADIS) method is developed to overcome these downsides of occupation risk analysis in the NGPC project within the CSF context. A prioritized weighted average (PWA) operator for complex spherical fuzzy numbers (CSFNs) is incorporated into the group risk evaluation matrix generation process, which can take the priority degrees of experts into account. Then, the extended CRADIS method-based Fine-Kinney framework is generated, in which the Choquet integral for CSFNs is incorporated to reflect the impact of interactive risk factors. Next, the detailed solution procedures of the framework for handling the occupational risk evaluation problem are presented. Finally, the described framework is employed as an empirical example of occupational risk analysis for the NGPC project to demonstrate its feasibility in practice. After that, a sensitivity analysis of the parameter is investigated to testify to the stability and rationality of the reported synthetical Fine-Kinney framework. Subsequently, to further display the advantages of the developed Fine-Kinney framework, a comparative study is implemented to discuss the evaluation result of occupational risk derived from the proposed framework and those of the existing similar Fine-Kinney frameworks. The analysis results indicate that the occupational risk (attack by human or animal) with the maximum risk priority value (1.000) using the framework is identified as the most serious risk for the NGPC project.

Reservoir Management (RM) is an example of sequential decision problems in the oil and gas industry. Therefore, implementing Decision Analysis (DA) tool to systematically resolve such problems has been a common practice. The value of Information (VOI) framework acts as one of these tools that helps reservoir engineers to manage RM problems. Regarding this, the Least-Squares Monte Carlo (LSM) algorithm, which is one of the simulation-regression approaches, has been employed to estimate VOI for a better quality of decision-making (DM). Integration of the LSM algorithm in RM is coined as “Sequential Reservoir Decision-Making” (SRDM). This approximate method is essential to resolve a sequential decision problem with high dimensionality caused by many possible outcomes of uncertainties. This challenge is generally known as the “curse of dimensionality”. In this work, a modified LSM algorithm has been applied under the SRDM paradigm to optimize the waterflooding initiation time considering geological uncertainties. The modification considers the effects of information acquired previously and at the current decision time before a decision is made. The reservoir model used in this work is the OLYMPUS benchmark model. Apart from utilizing Linear Regression (LR) in the LSM algorithm, the use of two machine learning (ML) techniques, viz. Gaussian Process Regression (GPR) and Support Vector Regression (SVR), have been illustrated to estimate the VOI. Based on the results, LR, GPR, and SVR correspondingly estimate the VOI as 11.52 million USD, 11.17 million USD, and 12.46 million USD. This means that SVR displays an improvement of 8.18% compared to the VOI assessed by LR. This shows its good applicability in VOI estimation and it can be concluded that integrating ML techniques into the SRDM paradigm demonstrates high potential for RM applications.

The emulsification phenomenon exists during crude oil exploitation in the Jimsar area of Xinjiang. In the field, the emulsification mechanism and its influence on production are unclear. To clarify the self-emulsifying law of crude oil and its influence on production during soaking, this study carried out microscopic visualization displacement experiments, spontaneous imbibition displacement, and oil–water displacement experiments. Results show that oil–water contact time and water phase type affect the size of the emulsified layer between crude oil and water phase. The oil–water distribution type affects the formation mode of crude oil emulsification. After the opening of the water phase channel, crude oil mainly migrates in the form of a water-in-oil emulsion. The crude oil attached to the pore wall and stuck in the pore throats is the main source of the dispersed phase in the emulsion. When crude oil moves through pores, the high-curvature boundary changes the interfacial tension and capillary force. Thus, this case makes the crude oil easily stuck to form small oil droplets and promotes the dispersion of crude oil to form an emulsion. In the tight core, a decrease in the spontaneous imbibition ability was attained by increasing nano-emulsion concentration. However, the oil displacement effect of 0.3% nano-emulsion is better than that of the 0.6% concentration. Therefore, ensuring a certain spontaneous imbibition ability and a certain displacement efficiency is necessary. Oil recovery can be enhanced to a certain extent after self-emulsification in the pores of Jimsar crude oil. When oil displacement depends on the capillary force or driving pressure difference, the oil–water interfacial tension is not the lower, the better, and a suitable range exists. The suitable oil–water interfacial tension in this region is between 0.1 and 1 mN/m.

Oil and gas exploration increasingly requires high-resolution imaging of small, irregularly shaped, and highly heterogeneous well-side complex and abnormal geo-bodies. Conventional borehole acoustic imaging is often unable to accurately obtain the position and azimuth of small-scale abnormal geo-bodies. This study presents an inversion method that uses scattered waves for borehole 3D acoustic imaging and an implementation scheme that combines plane and spherical scanning imaging. The finite-difference time-domain method was used to simulate the acoustic fields for borehole azimuthal acoustic imaging of one and two caves next to a well. The proposed inversion method of 3D spatial scanning based on multi-mode wave information was validated through numerical simulations investigating the effect of different parameters on the imaging results. The simulation results show that the cave-scattered waves include the PP-, PS-, SP-, and SS-waves. When plane scanning imaging is performed based on a single wave mode, the other wave modes become interference factors. After the weighted processing of the PP-, PS-, SP-, and SS-waves, plane scanning imaging based on multi-mode scattered acoustic waves is shown to weaken pseudo-solutions, enhance the signal-to-noise ratio, and improve the radial and axial positioning accuracy of scatterers. When the scatterer is close to the borehole axis, the echo received by the receiver is not a real plane wave. In contrast with the 3D slowness time coherence (STC) and beamforming methods, spherical scanning imaging based on single-mode scattered acoustic waves completely considers this fact, which improves its azimuth positioning accuracy. Furthermore, spherical scanning imaging based on multi-mode scattered acoustic waves accurately estimates the azimuth of caves beside a well with a high imaging resolution. Finally, numerical simulation results were validated using the field measurement data of a well, and the actual imaging effect of the new method was tested. Therefore, rather than using single-mode reflected waves with limited information, the proposed method of scanning imaging using scattered acoustic waves can substantially improve the imaging resolution and positioning accuracy of small-scale abnormal geo-bodies beside a well and enhance the detection range.

Significant natural fractures develop in deep sandstone reservoirs. However, the previous numerical simulation for matrix acidizing in sandstone rocks always focused on the reactive flow in porous media. A series of responses between multiple types of acids and minerals in fracture and matrix pores have been a significant setback for seeking the mechanism of acid flow in fractured sandstone rocks. This paper, established a multistage reactive-transport model for fractured sandstone rocks based on the two-scale continuum model to explore the effects of fractures on the reactive flow. The weak forms of fluid flow and solute transport equations are derived using the Galerkin method to couple the matrix and fracture domains, and the extended finite element method was used to solve the discretization model. Additionally, we presented numerical simulations under 2D linear flow conditions with specific and sensitive analyses about fracture and matrix properties. Numerical cases showed that the wormhole-shaped structure generated by acid dissolution is hard to develop even in highly heterogeneous fractured sandstone rocks due to the low reaction rate between mud acid and minerals. However, fractures that are not parallel to the flow direction can accelerated acid flow in the formation and reduced Si(OH)₄ precipitation. Increasing the injection rate can not change the dissolution patterns of sandstone, but expanded the acid diffusion range and decreased Si(OH)₄ precipitation in fractured sandstone.

An equivalent permeability approach can upscale the discrete fracture network (DFN) model to an equivalent DFN model and significantly reduce the gas flow simulations in a large-scale fractured gas reservoir. Current equivalent permeability prediction models are only applicable to the reservoir with a simple fracture network. However, an equivalent permeability prediction model has not been available for a reservoir with a multiscale discrete fracture network. This study proposes a multiscale convolutional neural network model (called MsNet) and introduces three mainstream structures with high performance convolutional neural network (CNN) (ResNet-18, VGG-16 and GoogLeNet) to efficiently predict the equivalent permeability of a complex multiscale fracture network. These CNN models use both the images and features of DFN as their input and the equivalent permeability as their output. This MsNet model is validated with the simulation results simulated by Lattice Boltzmann method and compared with the three mainstream CNN structures and an existing permeability prediction model (CNN-4). It is found that this MsNet model innovatively considers the multiscale characteristics of DFN by a multiscale convolution feature fusion and combines the residual connection for further performance enhancement. Both DFN dataset and MsNet model structure affect the model prediction ability. A deeper network structure of MsNet model can enhance its prediction ability, but significantly increases training time. The MsNet-8-4 (a MsNet structure with 8 multiscale connection modules and 4 sub-networks in each module) has the least convergence time and the lowest mean absolute error on the test set. It performs obviously better than other four models on the DFN dataset with higher fracture density. The MsNet model can well accelerate the simulation on the gas flow in a complex discrete fracture network.

Free gas in a marine drilling riser presents a hazardous situation as the gas can quickly expand to produce dangerous gas volumes at the surface. However, the conventional gas kick detection methods, that rely on surface measurements and data from point sensors or gauges, are often inadequate to predict the dynamic behavior of a given amount of gas entering the riser. This study presents comprehensive results from well-scale experiments that demonstrate novel insights into the real-time gas rise behavior across a 5163-ft-deep wellbore using distributed fiber-optic sensors. The experimental well simulates an offshore marine riser-like scenario with its larger than average annular space and fluid circulation capability at high pressures and rates. Thus, the experimental and numerical model results in this study provide useful insights on gas rise dynamics in a large annular space along long intervals, which are relevant for studying gas in marine risers.

Distributed acoustic sensor (DAS) and distributed temperature sensor (DTS) results from eight sets of well-scale tests are presented to investigate the effect of gas kick volumes (from 2 bbl to 15 bbl), circulation rates (from 0 to 200 GPM), and gas injection methods (through tubing or a ½-in. capillary injection line), on gas rise dynamics in the wellbore. Since slow-moving gas bubbles create small vibration and temperature effects, a variety of time- and frequency-domain signal processing techniques are developed to analyze the Fiber data were processed using frequency band energy (FBE), time-frequency scalograms, energy spectrums, frequency-wavenumber (FK) transform, and signal-to-noise ratio analysis. Gas velocities measured independently from DAS and DTS were validated using a numerical model, as well as with downhole pressure gauge data analysis, demonstrating good agreement for all eight trials. The numerical model presented in this study was validated with the downhole gauges and presents many useful insights for gas-in-riser conditions, such as gas arrival at the surface and rate of pressure build-up in closed wells.

Lithology identification using geophysical log information is vital for log interpretation and reservoir evaluation. As a result of the highly similar features for log curves that characterize complex lithology, there is significant information redundancy regarding the process of lithology identification. In addition, as a result of the highly nonlinear characteristics of log curves, the mapping relationship with lithology has certain ambiguities and uncertainties, which affect the lithology prediction results. Combining principal component analysis (PCA) and the fuzzy decision tree (FDT) model, we propose a new intelligent lithology identification method that is capable of effectively solving these problems well. However, because of the inaccuracy for empirically set parameters, an adaptive fuzzy decision tree algorithm based on particle swarm optimization (PSO-FDT) was proposed after analyzing the main features of the fuzzy decision tree and using an improved particle swarm optimization (PSO) algorithm to determine the relevant parameters. Compared with the FDT algorithm which determines parameter values empirically, the performance of the PSO-FDT has been significantly improved. Finally, the proposed PSO-FDT model was verified using test data. Experiments confirm that the proposed model is more effective than other lithology identification models. The identification accuracy for all lithologies was equal to or greater than that of the other methods. In addition, the overall accuracy was improved by at least 9.71%.