CN-122020999-A - Intelligent design method and system for in-situ leaching permeability-increasing parameters

CN122020999ACN 122020999 ACN122020999 ACN 122020999ACN-122020999-A

Abstract

The invention discloses an intelligent design method and system for on-site leaching permeability-increasing parameters, wherein the method comprises the steps of obtaining input data of a target on-site leaching site; the method comprises the steps of constructing a seepage channel characterization result based on input data, identifying and obtaining a dominant seepage channel according to the seepage channel characterization result, constructing a channel network model based on the dominant seepage channel, calculating a channel connectivity index according to the channel network model, constructing uncertainty description and uncertainty constraint, solving an seepage increasing parameter under the uncertainty constraint, and outputting a seepage increasing parameter scheme. According to the method, through quantitative characterization channel characteristics and robustness design, the on-site debugging and error testing cost is reduced, the controllability and consistency of the on-site leaching permeability-increasing channel are improved, and the uranium recovery efficiency is effectively improved.

Inventors

LI XIFAN
JIANG QUAN
ZOU YAN
DONG ZHIWEI
CAI YANGYANG
LIU LEI

Assignees

中国科学院武汉岩土力学研究所

Dates

Publication Date: 20260512
Application Date: 20260119

Claims (16)

1. The method for acquiring the permeability increasing parameter scheme is characterized in that the permeability increasing parameter scheme is used for guiding the in-situ leaching permeability increasing operation, and comprises the following steps: S1, acquiring input data of a target field leaching site, wherein the input data comprises one or more of stratum structure data, injection well arrangement data, injection working condition data and production monitoring data; s2, constructing a seepage channel characterization result based on input data, and identifying and obtaining an dominant seepage channel according to the seepage channel characterization result; S3, constructing a channel network model based on the dominant seepage channel, wherein the channel network model represents the node-side connection relation of the dominant seepage channel and the reachable relation between the injection end and the extraction end; S4, calculating a channel connectivity index according to the channel network model, wherein the channel connectivity index represents the penetration degree between the injection end and the extraction end, the contribution degree of the main channel and/or the bypass diversion degree; S5, constructing uncertainty description and uncertainty constraint, wherein the uncertainty description characterizes the influence of various uncertainty factors on channel connectivity indexes, and the uncertainty constraint limits that the channel connectivity indexes meet preset requirements under various disturbance scenes; And S6, solving the permeability increasing parameter under the uncertainty constraint, and outputting a permeability increasing parameter scheme, wherein the permeability increasing parameter scheme comprises one or more of injection pressure, injection flow and injection ratio.
2. The method for obtaining a permeability increasing parameter scheme according to claim 1, wherein the stratum structure data comprises porosity, permeability data and non-uniformity parameters, wherein the porosity and the permeability data are obtained through core or logging interpretation, and the non-uniformity parameters are obtained through geophysical inversion; The injection and production working condition data comprise injection pressure and flow time sequence data; The production monitoring data comprise inter-well communication test data, tracer or solute transport monitoring data, liquid production amount and grade time sequence data; and carrying out data cleaning, abnormal rejection and unified scale normalization processing on the input data before constructing the channel network model.
3. The method for obtaining a permeability-increasing parameter profile according to claim 1, wherein the characteristics of the permeability channel are obtained based on one or more of the following methods: 1) A flow density field or a velocity field obtained based on porous medium seepage numerical simulation; 2) Inverting the obtained equivalent permeability field based on the monitoring data; 3) Pore/fracture connected domain based on imaging or segmentation; the dominant seepage channel is obtained through threshold segmentation, connected domain screening and morphological treatment.
4. The method for obtaining the permeability-increasing parameter scheme according to claim 1, wherein the channel network model is obtained by performing skeleton extraction and topology construction on a dominant permeability channel, nodes are used for representing channel intersection points, end points or key turning points, edges are used for representing channel segments, and the nodes or edges are given weights, and the weights comprise channel traffic capacity, equivalent channel width, channel length, tortuosity and local permeability.
5. The method of claim 1, wherein the channel connectivity indicator comprises one or more of: The accessibility index is used for representing whether a through path exists between the injection end and the extraction end and the quantity of the through paths; the minimum cost penetration index is used for representing the optimal penetration cost from the injection end to the extraction end under the condition of considering the edge weight; The main channel contribution index is used for representing the duty ratio of a plurality of channel segments with the largest contribution to the penetration in the total channel weight; And the bypass shunt index is used for representing the bypass scale, the bypass quantity and/or the bypass weight ratio deviating from the main through path.
6. The method for acquiring the permeability-increasing parameter scheme according to claim 1, wherein the uncertainty description is realized by an uncertainty parameter set or a scene set, wherein the uncertainty parameter set or the scene set comprises one or more of permeability and porosity disturbance, channel identification threshold disturbance, well pattern boundary disturbance and injection system disturbance; The uncertainty constraint includes one or more of a robust lower bound constraint, a robust upper bound constraint, a confidence lower bound constraint, a confidence upper bound constraint.
7. The method for obtaining the permeability increasing parameter scheme according to claim 6, wherein the uncertainty constraint comprises that a channel connectivity index is not lower than a connectivity threshold value in any uncertain scene and/or a bypass shunt index is not higher than a shunt threshold value in any uncertain scene, and the connectivity threshold value and the shunt threshold value are determined by a recovery rate target, an environment risk control requirement and an energy consumption constraint requirement.
8. The method of claim 1, wherein the permeability increasing parameters include one or more of a number of stages of staged injection, a duration of each stage, a pressure/flow sequence of each stage, a pulse injection period and duty cycle, an interval time, well group proportioning parameters, and the permeability increasing parameters meet an upper equipment capacity limit, an upper wellhead pressure limit, and/or an upper total allowable injection amount limit.
9. The intelligent design method for the in-situ leaching permeability increasing parameter according to claim 1, wherein the intelligent solving method for the permeability increasing parameter comprises the following steps: generating a candidate permeability increasing parameter set, wherein the candidate permeability increasing parameter set is generated through parameter space sampling, rule generation and historical scheme migration; carrying out feasibility screening on the candidate permeability increasing parameter set; And (5) selecting feasible candidates under the condition that uncertainty constraint is met.
10. The intelligent design method of the in-situ leaching permeability increasing parameter according to claim 9, wherein the optimization of the feasible candidates is performed by taking connectivity improvement as a target and considering bypass shunt inhibition, wherein the optimization comprises an optimization process of maximizing channel connectivity indexes and/or minimizing bypass shunt indexes, and a constraint or punishment item is set for one or more of an energy consumption index, an injection total amount index and a scheme switching frequency index.
11. The intelligent design method for the in-situ leaching permeability increasing parameter is characterized by comprising the steps of constructing a proxy model, wherein the proxy model is used for predicting channel connectivity indexes corresponding to different permeability increasing parameters under the condition of not performing full numerical simulation, the proxy model is trained by historical working condition data, numerical simulation data and field test data, and the proxy model is one or a combination of a regression model, an integrated learning model and a neural network model.
12. The intelligent design method for the on-site leaching permeability increasing parameter according to claim 1, wherein on-site leaching is carried out according to a permeability increasing parameter scheme, and an on-site leaching process is detected to obtain monitoring data; When the deviation between the channel connectivity index represented by the monitoring data and the predicted value of the permeability increasing parameter scheme exceeds a preset threshold, updating the input data and outputting the updated permeability increasing parameter scheme.
13. The intelligent design method for the in-situ leaching permeability increasing parameter according to claim 1, wherein the output permeability increasing parameter scheme comprises one or more of a control instruction of a target well group or a target well, a staged injection system parameter, a constraint satisfaction description and a robustness evaluation result, and the robustness evaluation result comprises a statistical range and/or a confidence boundary of a channel connectivity index under an uncertain scene set.
14. An intelligent design system for an in-situ leaching permeability increasing parameter, which is characterized by comprising: The system comprises a data acquisition and preprocessing module, a data processing module and a data processing module, wherein the data acquisition and preprocessing module is used for acquiring input data of a target field leaching field, and the input data comprises one or more of stratum structure data, injection well arrangement data, injection well working condition data and production monitoring data; The channel characterization and identification module is used for constructing a seepage channel characterization result based on input data and identifying an dominant seepage channel according to the seepage channel characterization result; the channel network construction module is used for constructing a channel network model based on the dominant seepage channel, wherein the channel network model characterizes the node-edge connection relation of the dominant seepage channel and the reachable relation between the injection end and the extraction end; the system comprises a connectivity index calculation module, a channel connectivity index calculation module and a control module, wherein the connectivity index calculation module is used for calculating a channel connectivity index according to a channel network model, wherein the channel connectivity index represents the penetration degree between an injection end and a extraction end, the contribution degree of a main channel and/or the bypass diversion degree; The uncertainty modeling module is used for constructing uncertainty description and uncertainty constraint, wherein the uncertainty description characterizes the influence of various uncertainty factors on channel connectivity indexes, and the uncertainty constraint limits that the channel connectivity indexes meet preset requirements under various disturbance scenes; And the parameter solving and outputting module is used for solving the permeability increasing parameter under the uncertainty constraint and outputting a permeability increasing parameter scheme, wherein the permeability increasing parameter scheme comprises one or more of injection pressure, injection flow and injection ratio.
15. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method for intelligently designing infiltration enhancement parameters according to any one of claims 1 to 13 when the computer program is executed by the processor.
16. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the immersion infiltration enhancing parameter intelligent design method according to any one of claims 1 to 13.

Description

Intelligent design method and system for in-situ leaching permeability-increasing parameters Technical Field The invention relates to the technical field of seepage control and permeability increasing parameter design In uranium ore on-site leaching (In-situ Leaching, ISL) exploitation, in particular to an on-site leaching permeability increasing parameter intelligent design method and system. Background The uranium ore on-site leaching is an in-situ mining method for realizing uranium leaching and recovery by injecting leaching liquid and forming a seepage channel in the ground. The leaching solution is influenced by the differences of stratum structures, pore/crack development and permeability distribution, the migration path and the distribution proportion of the leaching solution in space are different, and the phenomena of insufficient effective leaching volume and the like caused by uneven distribution of the injection solution, local short circuit, bypass flow, dominant channel and the like can occur in the on-site leaching process, so that the uranium recovery efficiency and the process controllability are influenced. In order to improve the in-situ leaching effect, the engineering generally adopts permeability increasing measures and is matched with injection and production system regulation, such as adjustment of injection pressure, flow, pressure difference between wells, injection and production ratio, sectional injection strategy, cooperative control of different well sections and the like. However, factors such as formation heterogeneity, parameter inversion errors, monitoring noise, working condition fluctuation and the like can influence channel formation and evolution, so that design schemes based on single deterministic parameters are inconsistent in different sections or different periods, multiple adjustments are often needed on site, cost is increased, and risks are difficult to quantify. In recent years, a multi-scale structure characterization and data driving method provides a new thought for parameter identification and regulation of an in-situ leaching process, and for example, correlation between parameters and output response is established through pore structure characterization, seepage response data analysis and other modes. However, the existing method still has the defect in converting the channel structural characteristics into evaluation indexes which can be directly used for design, meanwhile, multisource errors and working condition fluctuation are not always introduced into design constraint, quantitative control on scheme robustness (robustness) is lacking, and the obtained parameters possibly deviate from expected effects under the action of stratum fluctuation or measurement errors. Therefore, an intelligent parameter design method for in-situ leaching and permeability increasing is needed, wherein connectivity indexes of seepage channels are used as core characterization values, corresponding relations between channel communication and effective leaching capacity are established, uncertainty caused by multi-source information, model simplification and measurement errors is explicitly considered in the parameter design process, steady design of permeability increasing parameters is realized through constraint and optimization, repeated debugging participation trial and error is reduced, and controllability and consistency of channel formation are improved. Disclosure of Invention The invention aims to provide an intelligent design method and system for on-site leaching permeability-increasing parameters, which are used for accurately solving and outputting a permeability-increasing parameter scheme for guiding on-site leaching permeability-increasing operation. In order to solve the technical problems, the invention provides a method for acquiring an enhanced permeability parameter scheme, wherein the enhanced permeability parameter scheme is used for guiding an in-situ leaching enhanced permeability operation, and the method comprises the following steps: S1, acquiring input data of a target field leaching site, wherein the input data comprises one or more of stratum structure data, injection well arrangement data, injection working condition data and production monitoring data; s2, constructing a seepage channel characterization result based on input data, and identifying and obtaining an dominant seepage channel according to the seepage channel characterization result; S3, constructing a channel network model based on the dominant seepage channel, wherein the channel network model represents the node-side connection relation of the dominant seepage channel and the reachable relation between the injection end and the extraction end; S4, calculating a channel connectivity index according to the channel network model, wherein the channel connectivity index represents the penetration degree between the injection end and the extraction end, the contribution degree of t