Detecting and locating old open holes is a major challenge in petroleum engineering. Electromagnetic methods work well for casing wells but are ineffective for open holes, which require new technologies. Remote detection acoustic reflection imaging logging is a reflection imaging technology that has emerged in recent years. It can now image fractures and vugs within tens of meters near the borehole by processing reflected wave data from full wave signals. This manuscript aims to test the practicality of using acoustic remote detection technology to locate and detect open hole wells. A 3-D elastic wave time-domain staggered nonuniform grid with fourth-order accuracy in space and second-order accuracy in time utilizing the finite difference method was used to conduct a numerical simulation of acoustic remote detection of open hole wells. The simulation analyzes the wave field and imaging characteristics of open holes in sandstone and mudstone formations at various distances from the rescue well. This analysis concluded that acoustic remote detection technology can successfully be used for open hole detection. Based on the change law of reflected wave amplitude, a set of open hole detection and positioning technology is developed. The calculated results exhibit small distance errors compared with the model, enabling accurate location determination within 4–16 m near the well. Therefore, remote detection acoustic reflection imaging holds great potential for detecting and locating open hole wells.

Remote detection acoustic reflection imaging logging is a cutting-edge imaging technology that enables precise interpretation of fracture and vuggy reservoirs. In this study, a 3-D elastic wave time-domain staggered nonuniform grid with fourth-order accuracy in space and second-order accuracy in time utilizing the finite-difference method was developed. Numerical simulations of the acoustic field near the borehole were conducted for dipole shear wave imaging, and the effects of fracture width, distance from fracture to the borehole, and hole radius on the reflected wave in acoustic reflection imaging logging under different fillings were analyzed. The radial resolution of the acoustic reflection imaging logging was studied, and the theoretical detection range was discussed. The simulation results show that the shear wave velocity has a significant impact on the reflected wave amplitude. Fractures filled with solid present a parabolic shape as the fracture width changes, while the amplitude of the fracture reflection wave filled with fluid decreases with the increase of the distance from the borehole but does not change with the width of the fracture. The reflected wave of the vug model has the characteristics of weak energy and multiple reflections, and the numerical simulation results of fracture and vug correspond well with models. Using the knowledge of forward modeling, the interpretation of fractures and vugs within 30 m of the borehole was carried out during the actual exploration. This technology significantly improves the detection range of the reservoir near the borehole.

Reservoir quality evaluation is an integral part of oil–gas exploration and development in fracture-vug carbonate formations. Varieties of geophysical data with different advantages are used in reservoir evaluation. These multisource data include conventional logs (CLs), borehole imaging logs (BILs), borehole acoustic reflection imaging logs (BARILs), and even seismic data. Obviously, combining these data can get richer formation information, which is more reliable than single-source data. In this work, the fusion dataset was constructed by calculating the principal components of CLs, BILs, and BARILs. The fusion dataset describes the fracture-vug information in different depths of investigation. Then, the agglomerative hierarchical clustering was used to analyze the fusion data and evaluate the reservoir quality in the borehole. Finally, taking the borehole-side seismic data and borehole evaluation results as training data, an intelligent model was trained based on the random forest algorithm. Also, the intelligent model can be used for reservoir quality classification in a 3-D space. In the case study, the evaluation results of reservoir quality are consistent with the validation data, and the accuracy of the final intelligent model is 85.15%.

Tight sandstone reservoirs are characterized by low porosity, extra-low permeability, and diverse mineral compositions for which previously established log interpretation methods are not suitable. Therefore, it is necessary to explore new log interpretation methods. The committee machine is a recently developed composite expert network. The regression committee machine (RCM) is constructed by combining a backpropagation neural network (BPNN), extreme learning machine (ELM), wavelet neural network (WNN), and using two different weight calculators for decision-making. Petrophysical models for tight sandstone reservoirs are integrated together with the RCM. The RCM and petrophysical model hybrid intelligent system for log interpretation is developed. The data process flow and specific implementation method of the log interpretation are designed. The tenfold cross-validation method is used to train and optimize the parameters of the RCM in the hybrid intelligent system. The hybrid intelligent system is applied to the Chang 8 tight sandstone of Yanchang Formation in the Ordos Basin, China. The case studies show that the predicted porosity, permeability, and water saturation by the RCM with petrophysical models are all consistent with the core measurement. The data and models jointly driven intelligent system is more accurate than petrophysical models and individual expert networks. This study effectively improves the formation evaluation for tight sandstone reservoirs.

The majority of previous studies of induction logging in fractured formations have typically focused on the variation in spatial resistivity distribution around the wellbore. However, the effect of cumulative charge on the boundaries of an inclined fracture with a distinct morphology on induction logging has rarely been discussed. In this work, the 3-D finite element method (3-D FEM) is adopted to investigate the dual-induction logging response of inclined fractures in formations of different resistivities considering the volume contribution and charge contribution. First, induction logging theory is studied, and a 3-D fractured formation model considering cumulative charges is established. Second, the induction logging responses under various fracture resistivities, apertures, dip angles, and formation resistivities are simulated and analyzed. Subsequently, the corresponding logging response characteristics are studied, and the different response laws of inclined fractures in high- and low-resistivity formations are systematically summarized. Third, a 1:5 miniaturized dual-induction logging instrument was designed and manufactured. Furthermore, two devices were built to measure the induction logging response of the inclined fracture (simulated by nanoscale silver film or metal wire mesh) in low- and high-resistivity formations. The results of the physical experiments are consistent with the numerical results, validating the accuracy of the numerical simulation. Both the results reveal that due to the existence of cumulative charge layers on the upper and lower inclined fracture boundaries, the induction logging response rules of the conductive fractures in high-resistivity formations differ from those in low-resistivity formations, and hence, they should be treated differently.