Oil Reservoir Model Research Articles

Mechanism evaluation of edge-water invasion mitigation by low temperature oxidation (LTO) coking in VHSD heavy oil reservoirs: A comparative study with traditional plugging techniques

Heavy oil is one of the globally important unconventional oil and gas resources, and its development faces the challenge of water invasion. The numerical model of a heavy oil reservoir with edge water is established and validated by field data and physical experiments. An evaluation model for selecting water invasion mitigation measures for different targets is established. The mechanisms of mitigating water invasion in the vertical-horizontal well steam drive (VHSD) heavy oil reservoir by cold-water injection, gel injection, nitrogen injection, and “Air + LTO” coking are investigated. The advantages of the “Air + LTO” coking technology in mitigating water invasion are elucidated. The importance of LTO reaction for water invasion mitigation is emphasized. The results show: (1) the edge water flows into the reservoir along the drainage interface of the steam chamber first and forms large water invasion channels in the heated crude oil region in the middle part of the reservoir; (2) the cold-water injection reduces the oil recovery factor by 3 %, the nitrogen injection increases it by 16.3 %, the gel injection improves it by 11.5 % and the “Air + LTO” coking technology enhances it by 24.1 %; (3) after air injection, the coke deposition effectively plugs the water invasion channels in the middle of the reservoir. This research provides a new perspective for mitigating water invasion and improving the oil recovery factor in heavy oil reservoirs with edge water, demonstrating the potential value and feasibility of the “Air + LTO” coking technology.

Research Progress of Three-dimensional Geological Modeling and Multi-field Coupling of Fractured Oil and Gas Reservoirs

Fractured oil and gas reservoirs play an important role in oil and gas exploration and development due to their complex fracture network structure. As the main fluid channel in the reservoir, fractures have a significant impact on the migration, accumulation and exploitation efficiency of oil and gas. This paper summarizes the key geological characteristics of fractured reservoirs, including fracture types, distribution rules and main controlling factors of fracture development, and discusses the influence of fractures on reservoir physical properties. Furthermore, the deterministic and stochastic modeling methods of three-dimensional geological modeling of fractured reservoirs and the latest progress of discrete fracture network modeling technology are introduced in detail. In addition, this paper also discusses in detail the application of multi-field coupling model in the numerical simulation of fluid flow in fractured reservoirs, including continuous medium model and discrete medium model, and their advantages and disadvantages in simulating fluid flow in fractured reservoirs. Finally, this paper summarizes the research progress of multi-physics coupling model of fractured reservoirs, and points out the direction and challenges of future research. Through these studies, it can provide theoretical support and technical guidance for oil and gas exploration and development, in order to achieve more effective development and management of fractured reservoirs.

Productivity Prediction Model of Tight Oil Reservoir Based on Particle Swarm Optimization–Back Propagation Neural Network

Single-well productivity is a crucial metric for assessing the effectiveness of petroleum reservoir development. The accurate prediction of productivity is essential for achieving the efficient and economical development of oil–gas reservoirs. Traditional productivity prediction methods (empirical formulae and numerical simulation) are limited to specific reservoir types. There are few influencing factors, and a large number of ideal assumptions are made for the assumed conditions when predicting productivity. The application scenario is ideal. However, in tight oil reservoirs, numerous factors affect productivity, and their interactions exhibit significant complexity. Continuing to use traditional reservoir productivity prediction methods may result in significant calculation errors and lead to economic losses in oilfield development. To enhance the accuracy of tight reservoir productivity predictions and achieve economical and efficient development, this paper investigates the tight reservoir in the WZ block of the Beibuwan area, considering the impact of geological and engineering factors on productivity; the random forest tree and Spearman correlation coefficient are used to analyze the main influencing factors of productivity. The back propagation neural network optimized by particle swarm optimization was employed to develop a productivity prediction model (PSO-BP model) for offshore deep and ultra-deep tight reservoirs. The actual test well data of the oilfield are substituted into this model. The analysis results of the example application yielded an RMSE of 0.032, an MAE of 1.209, and an R2 value of 0.919. Compared with traditional productivity prediction methods, this study concludes that the model is both reasonable and practical. The calculation speed is faster and the calculation result is more accurate, which can greatly reduce productivity errors. The model constructed in this paper is well suited for predicting the productivity of tight reservoirs within the WZ block. It offers substantial guidance for predicting the productivity of similar reservoirs and supports the economical and efficient development of petroleum reservoirs.

An echo state network approach to data-driven modeling and optimal control of carbonate reservoirs with uncertainty fields

Previous studies of reservoir simulation have shown that it is required to have every inherent carbonate reservoir physics available at the model development phase. However, this process while ideal is computationally intensive and time consuming especially for complex geological fields. In this study, Echo State Network (ESN) is proposed to predict two cases of reservoir waterflooding with integrated uncertainty properties in unstructured physical dimension and log-normal permeability fields. The generalization efficiency of ESN with respect to uncertainties in injection and production data is also investigated by tuning the ESN reservoir size (N). Consequently, adjoint based method is used to optimize the Net Present Value (NPV) via obtained injection well controls. Observation revealed that the developed ESN exhibits superior predictive accuracy in scenarios of low geological uncertainties, achieving an average test accuracy of 90.79%, and then declining to 86.19% as the uncertainty level is increased. Also, N is found to be significant in terms of ESN generalization efficiency with N = 40 achieving higher performance compared to N = 20, thereby validating its influence on ESN reservoir state to adapt to current data history during training. Lastly, the optimized scenario, derived from the initial well control predictions, demonstrated a higher NPV when compared with the base scenario of the developed oil reservoir model.

Reservoir Simulations of Hydrogen Generation from Natural Gas with CO2 EOR: A Case Study

This paper addresses the problem of hydrogen generation from hydrocarbon gases using Steam Methane Reforming (SMR) with byproduct CO2 injected into and stored in a partially depleted oil reservoir. It focuses on the reservoir aspects of the problem using numerical simulation of the processes. To this aim, a numerical model of a real oil reservoir was constructed and calibrated based on its 30-year production history. An algorithm was developed to quantify the CO2 amount from the SMR process as well as from the produced fluids, and optionally, from external sources. Multiple simulation forecasts were performed for oil and gas production from the reservoir, hydrogen generation, and concomitant injection of the byproduct CO2 back to the same reservoir. EOR from miscible oil displacement was found to occur in the reservoir. Various scenarios of the forecasts confirmed the effectiveness of the adopted strategy for the same source of hydrocarbons and CO2 sink. Detailed simulation results are discussed, and both the advantages and drawbacks of the proposed approach for blue hydrogen generation are concluded. In particular, the question of reservoir fluid balance was emphasized, and its consequences were presented. The presented technology, using CO2 from hydrogen production and other sources to increase oil production, also has a significant impact on the protection of the natural environment via the elimination of CO2 emission to the atmosphere with concomitant production of H2.

Optimal control of multilateral waterflooding wells in carbonate reservoirs with uncertainty consideration

The benefits of adjoint formulation in studying and improving the economic and production viability of reservoir waterflooding problems has been reported in several researches. However, there is little or no recorded report on its use for multilateral wells in uncertain waterflood reservoir conditions. This study aims to investigate the effect of geological uncertainty to multilateral well performance using adjoint formulation in oil reservoir waterflooding. First, an oil reservoir model with uncertainty assumption in permeability, Corey value, and physical dimension in reservoir compartmentalization was developed. Second, an optimal control sequence for the multilateral injection well was developed to optimize the net present value (NPV). The study revealed that with the obtained control sequence, reservoir compartmentalization notably faults in rock structure, significantly influenced the production capacity of multilateral wells which resulted in a higher NPV and oil rate when compared to other uncertainty cases.

Stochastic lithofacies and petrophysical property modeling for fast history matching in heterogeneous clastic reservoir applications

For complex and multi-layered clastic oil reservoir formations, modeling lithofacies and petrophysical parameters is essential for reservoir characterization, history matching, and uncertainty quantification. This study introduces a real oilfield case study that conducted high-resolution geostatistical modeling of 3D lithofacies and petrophysical properties for rapid and reliable history matching of the Luhais oil reservoir in southern Iraq. For capturing the reservoir's tidal depositional setting using data collected from 47 wells, the lithofacies distribution (sand, shaly sand, and shale) of a 3D geomodel was constructed using sequential indicator simulation (SISIM). Based on the lithofacies modeling results, 50 sets of porosity and permeability distributions were generated using sequential Gaussian simulation (SGSIM) to provide insight into the spatial geological uncertainty and stochastic history matching. For each rock type, distinct variograms were created in the 0° azimuth direction, representing the shoreface line. The standard deviation between every pair of spatial realizations justified the number of variograms employed. An upscaled version of the geomodel, incorporating the lithofacies, permeability, and porosity, was used to construct a reservoir-flow model capable of providing rapid, accurate, and reliable production history matching, including well and field production rates.

Silica aerogel nanoparticle-stabilized flue gas foams for simultaneous CO2 sequestration and enhanced heavy oil recovery

With declining conventional reserves, tapping heavy oils generates substantial emissions yet is essential to meet energy demands. This study pioneers an integrated approach using silica aerogels to develop thermally stable flue gas foams for enhanced CO2 sequestration and heavy oil recovery. Microfluidic experiments and molecular dynamics simulations revealed a 5-fold decrease in gas diffusion rates for aerogel-stabilized versus pure surfactant foams at 95 °C due to the ultralow thermal conductivity of the nanoparticle coating layer. The thermally stable nanoengineered foams demonstrated a remarkable 81.23% CO2 trapping efficiency in heterogeneous cores, surpassing conventional flue gas foam. Additionally, three-dimensional simulations in an oil reservoir model showed improved vertical conformance and oil recovery with aerogel-stabilized foam. The exceptional insulating properties redirected heat downward to enhance sweep efficiency. This novel concept transforms flue gas emissions into value-added foams for concurrent environmental and productivity benefits during heavy oil development.

Fracture Modeling of Shale Oil and Gas Reservoirs in Texas

Formation fracturing is the method of choice for developing shale oil and gas reservoirs that constitute a gigantic resource in the U.S.A. and many other countries but are characterized by a low permeability in the nano-Darcy range. The oil production of Texas has increased by about 5 million B/D in 15 years as a result of shale exploitation by massive multistage hydraulic fracturing. The mathematical modeling of this fracturing process is complex and can be approached in several ways. This paper first gives a concise description of the fracturing process as carried out in Texas. Included are the ranges of the key reservoir properties, as well as the injection fluid volumes and pressure, the composition of the injected fluid, proppant type, and volume, and other relevant data. Also included are the number of fracture stages, methods of zonal isolation, and diagnostic techniques used. An important variable considered is flowback, in particular, fluid retention and oil and gas production. High-salinity water production is discussed. Given the above variables, the currently used fracture simulators are briefly considered and compared, both the geomechanics-based and fracture-propagation-based. No single simulator can model the complete process. The directions currently being followed are briefly described. Also discussed is the simulation of the re-fracturing process and its range of success in increasing oil recovery from about the original ~5% to ~8%. Future processes such as plasma fracturing are mentioned, and their future applicability is discussed for further increasing oil recovery.

Rapid evaluation of capillary pressure and relative permeability for oil–water flow in tight sandstone based on a physics-informed neural network

Efficient and accurate evaluation of capillary pressure and relative permeability of oil–water flow in tight sandstone with limited routinely obtainable parameters is a crucial problem in tight oil reservoir modeling and petroleum engineering. Due to the multiscale pore structure, there is complex nonlinear multiphase flow in tight sandstone. Additionally, wetting behavior caused by mineral components remarkably influences oil–water displacement in multiscale pores. All this makes predicting capillary pressure and relative permeability in tight sandstone extremely difficult. This paper proposes a physics-informed neural network, integrating five important physical models, the improved parallel genetic algorithm (PGA), and the neural network to simulate the two-phase capillary pressure and relative permeability of tight sandstone. To describe the nonlinear multiphase flow and the wettability behavior, five physical models, including the non-Darcy liquid flow rate formula, apparent permeability (AP) formula, and contact angle-capillary pressure relationship, are coupled into the neural network to improve the prediction accuracy. In addition, the input parameters and the structure of the physics-informed neural network are simplified based on analyzing the change rule of the oil–water flow with the main controlling factors, which can also save training time and improve the accuracy of the neural network. To obtain the data for training the coupled neural network, the dataset of tight sandstone in Ordos Basin is constructed with experimentally measured data and various fluid flow properties as constraints. The test results demonstrate that the estimated capillary pressure and relative permeability from the physics-informed neural network are in good agreement with the test ones. Finally, we have compared the physics-informed neural network with the quasi-static pore network model (QSPNM), dynamic pore network model (DPNM), and conventional artificial neural network (ANN). The calculation time of QSPNM and DPNM are hundreds of times longer than that of the physics-informed neural network. The coupled neural network has also performed much better than the conventional ANN. As the heterogeneity of pore spaces in tight sandstone increases, the advantages of the physics-informed neural network over ANN are more prominent. The prediction models generated in this study can estimate the capillary pressure and relative permeability based on only four routine parameters in a few seconds. Therefore, the physics-informed neural network in this paper can provide the potential parameters for large-scale reservoir simulation.

Experimental Study of Catalytically Enhanced Cyclic Steam-Air Stimulation for In Situ Hydrogen Generation and Heavy Oil Upgrading

The current study was performed for the experimental modeling of cyclic steam-air injection in a heavy oil reservoir model of dual porosity in the presence of a nickel-based catalyst for in situ oil upgrading enhanced by simultaneous hydrogen generation. The research was realized in the combustion tube setup with a sandpack core model under reservoir conditions due to the consistent injection of air followed by oil in situ combustion (ISC) and steam (water) injection. As a result, the original oil was upgraded regarding fractional composition and oil properties. In addition, simulated reservoir heterogeneity and cyclic stimulation intensified the hydrogen synthesis, which, in turn, could also contribute to oil upgrading.

Simulation of liquid production and water cut dynamics using fluid flow model and neural networks

In the oil industry, there is a noticeable tendency to use proxy modeling of various levels of complexity to perform operational predictive calculations, in particular machine learning methods that are actively developing in the context of digitalization and intellectualization of production processes. In this paper, using the example of a synthetic oil reservoir model development element, we present an approach to the joint use of a physically meaningful fluid flow model and machine learning methods for solving adaptation and prediction problems. A feature of the considered synthetic model is the presence of a pronounced zonal inhomogeneity of the permeability field. Within the framework of the proposed approach, a single-phase filtration model, simplified in comparison with the original formulation was used, the history matching of which was carried out by restoring the field of reservoir filtration parameters using a network of radial basis functions. Based on the reconstructed field, the connection coefficients between the wells were calculated, which qualitatively and quantitatively correspond to the true well connections. The next step was to train a recurrent neural network in order to predict the water cut of the produced fluid. The use of a recurrent neural network made it possible to reproduce the characteristic non-monotonic behavior of the water cut of the produced fluid, caused by non-stationary modes of operation of injection and production wells. A combination of the presented models makes it possible to predict the volume of the produced fluid and its phase composition. To assess the predictive properties of the models, the actual data set was divided into training and test intervals.

Seismic physical modeling study for a shale oil reservoir with complex lithofacies and meter-scale structures

Shale oil reservoirs usually have various lithofacies with complex spatial patterns. Seismic reflections usually reveal the combination of sets of thin mud shales with various lithofacies. The seismic response characteristics of the lithofacies and the differences in seismic response of different lithofacies are still unclear, making the reservoir description difficult. The complex tectonic structures, diverse lithofacies, thin interlayers, fractures with various length scales, and various fracture development zones in the shale oil reservoir have placed very high demands on the design and construction method during the physical modeling of the shale oil reservoir. Facing these challenges, a physical modeling study on shale oil reservoirs with complex lithofacies and meter-scale structures is conducted. The target is the shale oil reservoir of the [Formula: see text] subsegment in the Jiyang depression. The complex physical model of a shale oil reservoir, with the thin stratigraphic layers and the complicated lithofacies and fracture patterns to date, has been designed and constructed in a laboratory. In total, 25 combinations of materials have been developed to simulate various lithofacies with different elastic parameters, and the error of the wave velocity simulation is less than 2.35%. The simulation of 1 m thick interlayers and the construction method for seismic physical models with meter-scale geologic structures has been realized. The quality control analysis finds that the simulation accuracy of small-scale geologic structure, complex lithofacies, and fractures is relatively reliable. Based on the physical model of the shale oil reservoir, high-density and wide-azimuth seismic data are acquired. Preliminary data processing and analysis find that the model layers have clear reflection characteristics, providing reliable data for the next step of seismic characteristics analysis of complex lithofacies shale oil reservoirs. The methodology has improved the capacity of seismic physical modeling to simulate complex oil and gas reservoirs, enabling the seismic physical simulation method to be applied in analyzing the commonly observed complex lithofacies, fractures, and meter-scale structures in unconventional oil and gas reservoirs.

Darcy–Benard–Oldroyd convection in anisotropic porous layer subject to internal heat generation

An anisotropic horizontal porous layer saturated with viscoelastic liquids of the Oldroyd-B type is explored to determine how the internal heat source affects thermal convection. As a momentum equation, a modified Darcy–Oldroyd model is used that takes into account the anisotropy of the porous layer. The energy equation is formulated in such a way that the influence of internal heat sources and anisotropy in thermal diffusivity on the stability criterion may be easily identified. The effects of anisotropy, viscoelasticity, and internal heat generation on the onset of thermal convection are investigated using linear stability analysis. It is understood that convection begins via an oscillatory mode instead of a stationary mode because viscous relaxation, thermal diffusions, and internal heat generation mechanisms compete with one another. Both steady and unsteady finite-amplitude convections are studied using nonlinear stability analysis with the truncated Fourier series method. The effect of different governing parameters on the system’s stability and on convective heat transfer is studied. The present investigation has been significantly validated by the recovery of several prior results as special situations. The findings presented in this work are anticipated to have significant implications for a number of real-world applications, including modeling of oil reservoirs, crude oil extraction, crystal growth, the pharmaceutical and medical industries, and the use of geothermal energy, among others.

OPTIMIZATION AND MANAGEMENT OF OIL FIELDS DEVELOPMENT PROCESSES BASED ON MODELING AND SYSTEM APPROACH

The issues of effective optimization and management of oil field development processes using mathematical models and methods of system analysis, which are relevant for oil science and practice, have been studied. Based on a system analysis, approaches are proposed to eliminate the shortcomings of known models of oil reservoirs based on the mass conservation equation.

Application of particle filter to assess uncertainty for reservoir state and parameter estimation

A bimodal water saturation distribution can be found around the waterfront during water flooding in an oil reservoir, leading to subsequent non-Gaussian distribution of water saturation. Therefore, application of the Gaussian based methods may result in poor history matching and corresponding parameter estimation. For the reliability of the oil reservoir model, it is very important to ensure the realistic update of water saturation during history matching at any phase of production from oil reservoirs. This work employs particle filter (PF) for water flooding reservoir data assimilation, considering the effect of “non-Gaussianity” in water saturation distribution around the shock front during updating model state vector. This paper investigates the performance of the PF in updating state variables, particularly water saturation and efficiency to approximation of posterior probability distribution. In addition, particle perturbation is implemented by applying “ensemble covariance” in resampling stage in order to diverse particles and thereby avoid particle collapse. The methodology is presented stepwise considering a one-dimensional (1D) case first and then two-dimensional (2D) five-spot water flood problem. In both cases, state variables are updated with the application of PF and ensemble kalman filter (EnKF). With respect to history and water saturation matching, and approximation of posterior probability distribution, the performance of EnKF is comparable with PF where the non-Gaussianity is weak. However, in the presence of strong non-Gaussianity, EnKF shows four times higher error compared to PF. Furthermore, between “ensemble covariance” and typical “randomization” cases, “ensemble covariance” case shows a higher number of particles participating in resampling stage in particle perturbation. Therefore, “ensemble covariance” case leads to 5.3% and 7.5% more accurate results in estimating reservoir permeability and porosity, respectively.

Diagenetic Facies Controls on Differential Reservoir-Forming Patterns of Mixed Shale Oil Sequences in the Saline Lacustrine Basin

The Permian Lucaogou Formation has developed mixed shale reservoirs, but there are few studies on the diagenetic facies, and the control effect of differential diagenesis on the reservoir capacity of shale oil reservoirs in this area is not clear. Therefore, shale samples of the Lucaogou Formation were systematically selected in this study, and through cast thin sections, field emission scanning electron microscopy, XRD mineral analysis, low-temperature nitrogen adsorption and high-pressure mercury injection experiments, the reservoir capacity of the shale oil reservoirs was evaluated from the perspective of diagenetic evolution. The results show that the shale oil reservoir of the Lucaogou Formation in Jimsar Sag is in the middle diagenetic stage A. The diagenetic evolution sequence is compaction—chlorite cementation—silica cementation—first-stage carbonate cementation—first-stage dissolution of authentic albite—illite/smectite mixed layer cementation—second-stage carbonate cementation—second-stage dissolution. The shale reservoirs are divided into five diagenetic facies: tuffaceous–feldspar dissolution facies, mixed cementation dissolution facies, chlorite thin-membrane facies, carbonate cementation facies and mixed cementation compact facies. Among them, the former two diagenetic facies have strong dissolution and weak cementation and are high-quality diagenetic facies, mainly characterized by large pore volume and good pore connectivity, with relatively low D2 values defined as the fractal dimension of mesopores. On the basis of the above research, three different control models of Lucaogou Formation shale oil reservoirs are proposed, including dissolution to increase pores, chlorite cementation to preserve pores, and strong compaction cementation to reduce pores. The quality of reservoirs developed in this model is successively high, medium, and low. This work can provide guidance for the fine characterization and grading evaluation of mixed shale oil reservoirs in saline lake basins and has important theoretical and practical significance for the prediction of shale oil “sweet spot” distribution.

Parametric analysis by numerical simulation of an oil reservoir analogue from the Maceió formation (Alagoas basin)

In the northern part of the Maceió Formation, Alagoas Basin, there is the Barreiras do Boqueirão outcrop, located in Japaratinga (AL). In this outcrop a faciological, petrographic and diagenetic characterization of the sandstone outcrop was performed to build a simplified oil reservoir model. The response of the petrographic characterization provided porosity data along with the diagenetic processes. In this way, the geological study was able to provide input for further analysis regarding flow behavior. A simplified physical model representing the outcrop was first built, and two other physical models: one representing the petrophysical data used by Lira (2004), whose study was also carried out in the same outcrop, and an advanced recovery model through the steam injection method. Thus, it was possible to verify that the simplified model of the Maceió Formation presented low oil recovery and the continuous steam injection model had the best results in hydrocarbon production. Finally, a parametric analysis of the operational conditions of the injection wells was carried out in order to verify the influence of steam quality and injection flow rate on hydrocarbon recovery. The model with advanced recovery was the one that obtained the highest oil recovery, whereas the Lira (2004) model, even with the insertion of steam in the reservoir, did not present optimistic results. As for the oil production, it presented a discrepant recovery factor in relation to the value that was actually found in this study, and the simplified model of the Maceió formation indicated the lowest oil recovery.

Oil Charging Power Simulation and Tight Oil Formation of Fuyu Oil Layer in Sanzhao Area, Songliao Basin

A thermodynamic and hydrocarbon composition model based on VTFlinc software was conducted based on the homogenization temperature of fluid inclusions and gas-to-liquid ratio, and the fluid inclusions’ trapped pressure was calculated. This software does not require the determination of individual inclusions, the composition of the simulation procedure is simple, and the simulation result has certain reliability. The VTFlinc software was used to calculate the trapped pressure of oil inclusions in the Fuyu tight reservoir of the Sanzhao area in the Songliao Basin. The results show the existence of two phases of oil charge. The first phase occurred at the end of the Nenjiang Formation at 79–74 Ma with an oil entrapped pressure of 16.922–19.068 MPa, and pressure coefficient of 1.18–1.26. During the second period in the Sifangtai Mingshui group at 69–64 Ma, the oil entrapped pressure was 20.216–28.830 MPa, and the pressure coefficient was 1.21–1.33. During the two periods, the pressure of the trapped oil inclusions is abnormal, and the pressure coefficient is above 1.2. Abnormal high pressure is the main driving force of hydrocarbon source rock and oil in the Fuyu Formation, as well as an important driving force for the lateral migration of oil in the tight reservoir. The reservoir formation model of the Fuyu tight oil reservoir in the Sanzhao area is a direct high-pressure charging and multi-channel migration accumulation model. The Q-1 source rock and Fuyu oil layer has a direct contact relationship between the source rock and the reservoir.

Comprehensive Evaluation of Rock Mechanical Properties and in-situ Stress in Tight Sandstone Oil Reservoirs

Comprehensive research on reservoir rock mechanics and in-situ stress properties combined with petrophysical experiments, logging models and numerical simulation is an important means to achieve efficient development of tight sandstone oil reservoirs. In this study, a large number of rock mechanics and acoustic experiments, full-wave train array acoustic wave tests, hydraulic fracturing data and three-dimensional finite element simulations were used to study the rock mechanical properties and in-situ stress characteristics of continental tight oil reservoirs in the Yanchang Formation. The results show that under uniaxial conditions, the tight sandstone samples mainly suffer from tensional ruptures. With the increase of confining pressure, the tight sandstone samples undergo obvious shear ruptures. When the confining pressure is loaded to 35 MPa, a typical vertical shear fracture will be formed. The hydraulic fracturing calculation results show that the in-situ stress state of the target layer satisfies σv (vertical principal stress)>σH (maximum horizontal principal stress)>σh (minimum horizontal principal stress). Based on the results of rock mechanics and acoustic tests, we have constructed the dynamic and static mechanical parameter conversion models of tight oil reservoirs and the logging interpretation model of current in-situ stress. Furthermore, the finite element method is used to simulate the three-dimensional structural stress field of the target layer. The simulations show that the horizontal principal stress distribution in the work area is consistent with the applied environmental stress. The σH of the target layer is mainly distributed in 32–50 MPa, and the σh is mainly distributed in 20–34 MPa. Both σH and σh are relatively high in the southern uplift of the work area; among them, σH is usually greater than 44 MPa, and σh is usually greater than 24 MPa. The northern part of the study area developed several grooved areas with relatively low stress values. The regions with high stress values are often distributed in bands, which may be related to the compression caused by the deformation of the strata. For shear stress, left-handed and right-handed regions usually alternate with each other. However, the extent of the left-handed area in the southern uplift area is larger than that of the right-handed area, indicating that the tight oil reservoirs in the study area are mainly affected by left-handed activities.