Systems and methods include a method for predicting geological formation tops. First well log data associated with a key master well is received. Formation data identifying tops of formations confirmed in the key master well is received. Merged key master well and formation data is generated in a dynamic time warping (DTW)-readable format by merging the first well log data with the formation data. Second well log data associated with a training well located in geographic proximity to the key master well is received. The second well log data is formatted into the DTW-readable format. A DTW function is executed to generate indices associated with the formation tops. The DTW function uses the merged key master well and formation data and the formatted second well log data as DTW function inputs. Predicted geological formation tops for the training well are predicted using the generated indexes.

A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.

Near real-time methodologies for maximizing return-on-fracturing-investment for shale fracturing. An example system can calculate, based on sonic data and density data, mechanical properties and closure stress of a portion of shale rocks for fracture modeling. The system can generate one or more rock mechanical models based on the mechanical properties and closure stress of the portion of shale rocks, and perform one or more fracture modeling simulations based on one or more treatment parameter values. Based on the one or more fracture modeling simulations, the system can generate a neural network model which predicts a fracture productivity indicator of an effective propped area (EPA) and/or an effective propped length (EPL), and calculate a return-on-fracturing-investment (ROFI) based on the EPA or EPL predicted by the neural network model.

A procedure that integrates petrophysics and rock typing taken from vertical well measurements, 3D seismic elastic properties and seismic attributes, and geostatistical modeling to build a 3D reservoir model is provided. The 3D reservoir model may be directly incorporated into horizontal fracture model designs. The developed 3D reservoir model for a subsurface volume may be used in a fracture model to optimize fracturing designs and maximize well performance.

Mapping is performed of chemostratigraphic signatures of hydrocarbon reservoirs in three dimensions. Up-scaled chemostratigraphic data from well cuttings and well cores are tied with high-resolution elastic wireline data at well locations in the reservoir. The wireline data is used to determine suitable seismic attributes for mapping the desired chemostratigraphic property, and are obtained by performing pre- and/or post-stack inversions. A multi-attribute template, derived from the well data, is developed to characterize the reservoir in terms of its chemostratigraphic properties. The seismic data is then inverted to map the chemostratigraphic signatures of reservoir in three dimensions.

A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

The invention discloses a dolomite reservoir prediction method and system based on well and seismic combination, and storage medium. The method steps include: obtaining the dolomite index characteristic curve through well log sensitivity analysis, and distinguishing the dolomite and limestone according to the difference in their response range; after the artificial intelligence deep learning is performed on the dolomite index characteristic curve of the drilling area, the dolomite index characteristic curve of the virtual drilling area is obtained; according to the dolomite index characteristic curve of the drilling area and the virtual drilling area, the post-stack seismic data is used for inversion to obtain the distribution and development status of the dolomite reservoir in the test area. The invention effectively distinguishes the dolomite and limestone through the dolomite index characteristic curve, and accurately predicts the distribution and development status of the dolomite reservoir in the test area with less wells.

A method is described for generating a reservoir property model based on the quality of a seismic inversion product. The method may include receiving a seismic inversion product volume, a seismic attribute volume, and well data from wells drilled in a subsurface volume of interest; identifying collocated cells in the seismic volumes which correspond to the well data; creating attribute vectors from the seismic volumes in each of the collocated cells and a range of neighboring cells; calculating a seismic inversion error magnitude property at the collocated cells; training a data analytics method to predict the observed seismic inversion error magnitude property; verifying that the data analytics method accurately predicts the seismic inversion error magnitude using cross-validation; generating an inversion quality volume; and generating the reservoir property model based on the inversion quality volume. The method may be executed by a computer system.

A method of calculating the temperature and/or porosity of a geological structure, wherein there is provided at least two geophysical parameters of the geological structure, the method including inverting the at least two geophysical parameters to estimate the temperature and/or porosity of the geological structure.

A method for determining a favorable time window of an infill well of an unconventional oil and gas reservoir, which comprises the following steps: S1, establishing a three-dimensional geological model with physical properties and geomechanical parameters; S2, establishing a natural fracture network model in combination with indoor core-logging-seismic monitoring; S3, calculating complex fractures in hydraulic fracturing of parent wells; S4, establishing an unconventional oil and gas reservoir model and calculating a current pore pressure field; S5, establishing a dynamic geomechanical model and calculating a dynamic geostress field; S6, calculating complex fractures in horizontal fractures of the infill well in different production times of the parent wells based on pre-stage complex fractures and the current geostress field; S7, analyzing a microseismic event barrier region and its dynamic changes in infill well fracturing; and S8, analyzing the productivity in different infill times, and determining an infill time window.