8results about How to "Increase the encounter rate" patented technology

The application discloses a dynamic shale gas horizontal well stratum dip angle prediction method, which comprises the following steps: establishing a shale reservoir velocity model; preparing a shale reservoir structure map, and designing depths of target points A and B of a shale gas horizontal well, wherein the target point A is a starting position of a horizontal section of the shale gas horizontal well, and the target point B is an ending position of the horizontal section of the shale gas horizontal well; calculating a stratum dip angle alpha of the whole horizontal section according to a horizontal distance and a longitudinal distance between the target points A and B; after entering the target point A, a stratum dip angle beta is predicted by using depth data of first and last points of a preset distance drilled this time, the velocity model is updated by using the depth data of the first and last points of the preset distance drilled this time, the structure map is prepared again, a stratum dip angle gamma of a next drilling distance of the horizontal section is predicted by using the prepared structure map again, and a reference stratum dip angle theta of the next drilling distance of the horizontal section is determined according to the three stratum dip angles alpha, beta and gamma, so as to serve as a guide for drilling of the horizontal well. The method can effectively improve a high-quality shale drilling ratio of the shale gas horizontal well, improve drilling efficiency of the horizontal well and save drilling cost.

The application discloses a method and device for adjusting a drilling trajectory of a horizontal well. First, a standard well used for stratum comparison with a well to be drilled is determined, and a horizontal well geosteering model is established in combination with geological data and drilling engineering data. A current well resistivity while drilling and a while drilling gamma curve are compared with a standard well logging curve, and a target point vertical depth is calculated. In combination with a real-time response of a while drilling azimuthal resistivity boundary detection curve, a landing point of a target layer is determined, and a suitable hole inclination angle is used to enter the layer. A drilling trajectory and a relative position relationship with a stratum interface are determined by fitting a while drilling simulation curve of the horizontal well geosteering model with a current well actual drilling curve. A stratum interface azimuth and a distance from the trajectory to the interface are determined in real time by developing a while drilling azimuthal resistivity boundary detection rapid inversion, and the horizontal section drilling trajectory is adjusted. In combination with logging data and directional actual drilling characteristics, a layer position where the trajectory is located is judged, and a sand body distribution and a stratum change trend are predicted in combination with a regional seismic profile, so that the drilling trajectory is pre-adjusted.

This invention belongs to the field of oil and gas reservoir development technology, specifically disclosing a method for establishing a high-precision stratigraphic model profile through well-seismic integration. The method comprises the following steps: S1, establishing an initial geological model; S2, calculating the stratigraphic lines of geological sub-layers; and S3, generating a new geological model. Based on seismic data and drilled vertical wells, this invention provides a high-precision stratigraphic model for horizontal wells to be drilled. It is suitable for complex models of single wells, multiple wells, and multiple stratigraphic layers, providing a basis for seismic geological guidance work during horizontal well drilling, improving the drilling rate of reservoir "boxes," and reducing drilling engineering risks.

This invention provides a geological steering method for horizontal wells in tight sandstone gas reservoirs. First, relevant data from the target well and adjacent wells are acquired. Then, using this data, the geological characteristics of the target sandstone body, its micro-structure, reservoir heterogeneity, and stratigraphic occurrence are determined. Next, using well logging interpretation from adjacent wells and geophysical channel characterization, a refined three-dimensional geological model based on seismic constraints is established to clarify the sandstone body distribution and interlayer development. A geological profile is established along the well trajectory to determine the location of the horizontal well trajectory. Finally, trajectory optimization is performed. This steering method effectively combines geological and seismic data, using three-dimensional geological modeling to reasonably predict the distribution of reservoir sandstone bodies and scientifically assess interlayer development. The geological steering method and its implementation effectiveness are effectively guaranteed, and it can meet the geological steering requirements of different gas reservoirs and well areas with significantly different geological characteristics.

The application relates to the technical field of oil and gas exploration and development, and is a deep reservoir quality prediction method based on rock facies-geochemical coupling, which comprises the following steps: obtaining geochemical parameters and physical property parameters of a layer section to be predicted; taking the geochemical parameters and the physical property parameters of the layer section to be predicted as inputs, inputting a reservoir quality coupling prediction model, and outputting a porosity calculation value and a reservoir quality grade prediction result of the layer section to be predicted; and the reservoir quality coupling prediction model comprises a multivariate regression model related to the geochemical parameters, the physical property parameters and the porosity and a reservoir quality grade classification model. The application eliminates the multi-solution property of a traditional reservoir quality prediction method from a geological mechanism by means of strong coupling modeling of rock facies and geochemical parameters, and significantly improves the deep and super-deep reservoir quality prediction accuracy. Compared with the prior art, the application can significantly improve the drilling rate of high-quality reservoirs.

The embodiment of the present application provides a kind of reservoir distribution prediction method, device and storage medium, belong to petroleum technical field.The prediction method includes: obtaining the logging data of reservoir at different logging depths, wherein the logging data includes the permeability, porosity, water saturation and shale content of the reservoir;Determine reservoir quality factor based on the logging data;And reservoir quality factor curve is formed based on the reservoir quality factor at different logging depths, and reservoir distribution prediction is carried out.The embodiment of the present application obtains reservoir quality factor based on permeability, porosity, water saturation and shale content, and the reservoir quality factor can accurately identify reservoir, so as to accurately predict reservoir distribution, which is beneficial to higher reservoir drilling rate for well deployment.

The application discloses a horizontal well abnormal guidance early warning method and device, a storage medium and equipment, and belongs to the technical field of oil and gas engineering drilling and completion. The method comprises the following steps: acquiring three-dimensional position coordinate data, GR data while drilling, drilling time data and lithology logging data of drilled wells in a target block as an input data set; simulating GR data while drilling of a new well according to the GR data while drilling of the drilled wells, combining the input data set, and obtaining the predicted depth of the drill bit of the new well through a horizontal section intelligent guidance judgment model; and judging whether an abnormality occurs in the drilling process according to the predicted depth and a target formation. The application constructs a horizontal section borehole trajectory prediction model in the drilling process based on an artificial intelligence method, solves the problems that a drilled position cannot be found in time and a drill bit cannot be automatically adjusted in the horizontal well drilling process, improves the horizontal well drilling efficiency and reservoir drilling rate, improves the single-well production and recovery rate of an oil and gas reservoir, and reduces the field construction cost.

The application provides a carbonate fracture-cave zone facies-controlled post-stack geostatistics inversion method. The inversion method comprises the following steps: obtaining reservoir and non-reservoir probability data bodies; obtaining cave-type reservoir probability data bodies and fracture-hole-type reservoir probability data bodies; taking the reservoir probability data bodies as three-dimensional lithofacies probability constraints of the fracture-cave zone and taking the non-reservoir probability data bodies as three-dimensional lithofacies probability constraints of the surrounding rock; taking the cave-type reservoir probability data bodies as three-dimensional lithofacies probability constraints of the cave and taking the fracture-hole-type reservoir probability data bodies as three-dimensional lithofacies probability constraints of the fracture hole; performing fracture-cave zone facies-controlled post-stack geostatistics inversion to obtain an inversion P-wave impedance body; using well logging P-wave impedance and porosity crossplot analysis to obtain a P-wave impedance and porosity regression curve; and converting the inversion P-wave impedance body into an inversion porosity body according to the P-wave impedance and porosity regression curve. The application solves the problem of low prediction accuracy of the existing technology for fault-controlled fracture-cave-type carbonate reservoirs.