12 results about "Pore fluid pressure" patented technology

This invention discloses a logging method for rapidly predicting collapse pressure in mudstone / shale formations, obtaining the formation depth h, formation pore fluid pressure gradient (FPG), formation lithology density (DEN), and formation clay content (V) of the target formation. sh The drilling fluid system used – the system influence coefficient ε and the clay influence coefficient ξ are determined based on the drilling fluid system – using the formula PCG=(0.1×DEN×g+FPG+ξ×V) sh The formation collapse pressure gradient PCG is calculated using the formula P(ε) / 2-1, where ε is the system influence coefficient of the drilling fluid system. c =0.01×PCG×h to calculate the formation collapse pressure P c It effectively overcomes the complex experimental process required by traditional methods to determine parameters such as the internal friction angle, cohesion, and Biot coefficient of the rock; it also overcomes the drawbacks of relying on personal experience for calculation, which involves many subjective factors, and avoids random interference that is difficult to quantify, program, and maintain stability.

The invention relates to the technical field of geotechnical engineering investigation, and particularly discloses a depth-adjustable sampling method for geotechnical investigation, which comprises the following steps: acquiring pore fluid pressure data in real time through a sensor group arranged at the front end of a sampler; based on the data, dynamically predicting an evolution trend of a pressure field in front of the sampler and generating a prediction result containing a pressure abnormal state and spatio-temporal information thereof; performing grading processing on the prediction result to obtain a pressure risk grade; carrying out collaborative decision making on the grade, the soil body sensitivity and the target sampling quality to generate a dynamic control strategy; and finally, the injection action of the sampler is adjusted in real time according to the strategy, the injection action comprises executing micro-amplitude withdrawing and re-injection circulation or gradual deceleration operation, and the control effect is ensured through closed-loop optimization. The method can actively inhibit the water attack and cavitation effect, and improves the quality and reliability of obtaining an undisturbed sample in a complex stratum.

A kind of method for discriminating cementation natural fracture control hydraulic fracture propagation mode, the present application belongs to the field of unconventional oil and gas reservoir fracturing reconstruction;By obtaining hydraulic diffusion parameter and crack front dynamic motion parameter, local pore fluid pressure is obtained by diffusion analysis, and then effective normal stress is obtained by correcting macroscopic normal total stress;And it is compared with the crack threshold to generate damage accumulation factor, and the interface mechanical parameter is obtained by combining the tensile reference value;Then compared with local tip tensile stress, the propagation mode result is output, and the crack threshold is updated by using feedback optimization parameter;By constructing the causal coupling link of crack tip motion-forepoling seepage-damage accumulation-strength attenuation, the actual mechanical state of natural fracture interface can be dynamically characterized, so that the accuracy of propagation mode discrimination can be greatly improved.

The application discloses a complex stratum well wall stability prediction method, system, device and medium, obtains measured stratum data of a drilled well reservoir section; calculates stratum pore pressure and rock mechanics parameters according to the measured stratum data; calculates the ground stress size according to the stratum pore pressure and the rock mechanics parameter profile; takes the additional density of the mud as the initial value, and takes the tectonic strain coefficients in the horizontal minimum principal stress direction and the horizontal maximum principal stress direction as the initial tectonic strain coefficients, obtains the initial pore fluid pressure, the horizontal minimum principal stress and the horizontal maximum principal stress of the stratum based on the measured stratum data, and can obtain the collapse pressure equivalent density and the fracture pressure equivalent density of the drilled well according to the collapse pressure equivalent density calculation formula and the fracture pressure equivalent density calculation formula; when the pressure corresponding to the drilling mud density is greater than the collapse pressure equivalent density and less than the fracture pressure equivalent density, the well wall is stable.

The invention discloses a fractured medium attenuation characteristic numerical simulation method based on an improved BISQ model, and relates to the technical field of geophysical exploration, and the method comprises the steps: S1, building a pore fluid pressure-displacement coupling equation of the improved BISQ model under a quasi-static condition as a target form equation; s2, establishing a representative fracture medium numerical model according to reservoir fracture characteristics; s3, carrying out an oscillation compression numerical simulation experiment, and simulating boundary disturbance of elastic waves on the model by controlling boundary conditions; and S4, calculating a complex stress-strain field generated when the model responds to boundary disturbance under different frequencies, and calculating a complex longitudinal wave modulus and a longitudinal wave attenuation factor by using an average value of a vertical stress-strain body. The quasi-static numerical simulation method provided by the invention can calculate seismic wave energy dissipation under the combined action of Biot flow and jet flow of the rock model; and the frequency-dependent seismic attenuation characteristics of the fluid-containing crack medium are represented.

The present application relates to the technical field of geotechnical engineering investigation, and specifically discloses a rock-soil investigation adjustable-depth sampling method, which acquires pore fluid pressure data in real time through a sensor group arranged at the front end of a sampler; based on the data, the evolution trend of the pressure field in front of the sampler is dynamically predicted, and a prediction result containing the pressure abnormal state and its space-time information is generated; the prediction result is processed in stages to obtain a pressure risk level; the level is used in a collaborative decision with the soil sensitivity and the target sampling quality to generate a dynamic control strategy; finally, the penetration action of the sampler is adjusted in real time according to the strategy, including performing a micro-retraction and re-penetration cycle or a gradual deceleration operation, and the control effect is ensured through closed-loop optimization; the present application can actively suppress water hammer and cavitation effect, and improve the quality and reliability of obtaining undisturbed samples in complex strata.

A method for evaluating the reservoir pressure field during in-situ heated shale oil extraction is applied in the petroleum development field. The method comprises the following steps: ① Constructing a three-dimensional grid model of the shale oil reservoir between two horizontal wells; ② Evaluating the porosity of each reservoir grid unit after compression based on the temperature field, porosity, and volumetric thermal expansion coefficient of the reservoir skeleton; ③ Evaluating the volume of each fluid in each reservoir grid unit under constant pore fluid pressure based on the reservoir temperature field, fluid saturation, fluid thermal expansion coefficient, and natural gas solubility characteristics in oil; ④ Evaluating the pore fluid pressure in each reservoir grid unit based on the compressibility coefficient of each fluid at the corresponding temperature, combined with the reduction in porosity and the volume of each fluid after thermal expansion, thereby obtaining the pressure field of the shale oil reservoir.

Provided in the embodiments of the present application are a method and apparatus for identifying the slip risk of a fault, and a program product. The method comprises: establishing a three-dimensional spatial grid model for a fault of a target formation series to be identified, and acquiring a grid point attitude of grids on the fault of said target formation series, wherein the grid point attitude comprises the dip angle and strike of each grid point; acquiring pore fluid pressure values of the grid surfaces of the grids; on the basis of a preset three-dimensional structural geological model, determining principal stress data on the grid surfaces of the grids; on the basis of the grid point attitude, the pore fluid pressure values of the grid surfaces of the grids, and the principal stress data of the grids, determining slip index values of the grids, wherein the slip index values indicate whether the grid surfaces of the grids will undergo slip; and on the basis of the number of grids that undergo slip, determining a slip risk coefficient of the fault of said target formation series. The method is used to achieve the effect of accurately determining a slip risk coefficient of a fault of a target formation series.

The invention discloses a method for evaluating the natural gas extraction amount and residual amount of a deep organic-matter-rich reservoir, and solves the problem that the existing method cannot accurately and quantitatively evaluate the natural gas extraction amount and residual amount of the deep organic-matter-rich reservoir. The method comprises the steps that S1, a target interval reservoir three-dimensional reservoir grid is built, and the original pore fluid pressure, the original free gas amount, the original dissolved gas amount, the original adsorption gas amount, the original oil content and the original water content of each grid unit of the reservoir are determined; s2, determining the natural gas starting pressure gradient of each grid unit of the reservoir; s3, determining the pore fluid pressure of each grid unit of the reservoir; and S4, determining the exploitation amount of free gas, adsorbed gas and dissolved gas in each grid unit of the reservoir and the remaining amount of the free gas, the adsorbed gas and the dissolved gas. The method can accurately and quantitatively evaluate the free gas exploitation amount, the adsorbed gas exploitation amount, the dissolved gas exploitation amount and the remaining amounts of the free gas exploitation amount, the adsorbed gas exploitation amount and the dissolved gas exploitation amount in the deep organic-matter-rich reservoir, and provides an important basis for better evaluation of the production state of a natural gas well, formulation of a development scheme, economic evaluation and the like.

This invention relates to a logging method for rapidly determining formation leakage pressure, obtaining formation lithology and reservoir space type, formation vertical depth h, formation pore fluid pressure gradient (FPG), and formation water density D at points of interest in the target interval. W and formation clay content V sh The influence coefficients ε1 (strata lithology) and ε2 (reservoir space type) are determined by stratigraphic lithology and reservoir space type; the formula CPL = CFP + ε1 × ε2 × V is used to determine these influence coefficients. sh Calculate the formation leakage pressure coefficient CPL at the point of interest in the target layer; using the formula PL = 0.1 × CPL × D. W The formation leakage pressure PL at the target layer point of interest is calculated using the formula PLG = PL / h. The formation leakage pressure gradient PLG at the target layer point of interest is then calculated using this formula. Calculating the formation leakage pressure gradient can fully reflect the essential characteristics of the formation, requires minimal data resources, has low implementation costs, is simple and easy to implement, and is more readily applicable in the field.

The invention provides a formation pore fluid pressure prediction method based on sample expansion and deep learning, and relates to the field of formation pore fluid pressure prediction.The method comprises the steps that pressure data and multi-type logging data are collected and preprocessed; carrying out sample expansion based on an Eaton formula to obtain a synthetic pressure label; aligning the synthesized pressure label with the preprocessed logging data according to depth, and constructing a data set of an input feature-label pair; based on an Inception Conv1D multi-branch convolution module and a double-layer LSTM (Long Short Term Memory) module, constructing a formation pore fluid pressure prediction model and training through a data set; logging data obtained after new well preprocessing are input into the trained formation pore fluid pressure prediction model, and a formation pore fluid pressure prediction result is output. According to the technical scheme, the safety and efficiency of resource exploration and development are improved.

The invention discloses a cloud monitoring method for pore fluid pressure in a muddy layer at a seabed muddy bottom and related equipment. The method comprises the following steps: arranging a multi-mode sensing node to a target area through a remote control underwater robot; acquiring original signal data of the target area based on a preset sampling frequency, performing local preprocessing to obtain second signal data, and transmitting the second signal data to the cloud platform through relay communication; calling a preset multi-dimensional data analysis model through the cloud platform to perform dynamic modeling and comprehensive evaluation on the change process of the pore fluid pressure to obtain an analysis result of the pore fluid pressure; performing visualization processing on the analysis result, and performing early warning judgment on the analysis result based on a preset early warning mechanism; based on historical signal data in the cloud platform and corresponding actual early warning records, self-learning is carried out through machine learning, and an early warning mechanism is optimized and adjusted. The method can realize intelligent analysis and accurate early warning of the pore fluid pressure, and can be widely applied to the technical field of data processing.