Patent ID: 12221358

DETAILED DESCRIPTION

As shown inFIG.1, a cascade water recycling intelligent system100for shale gas exploitation includes a cascade water recycling system1, wherein a data output end of the cascade water recycling system1is connected with a data input end of an information system2, a data output end of the information system2is connected with a data input end of a decision making system3, and a data output end of the decision making system3is connected with a regulator4;

the cascade water recycling system1includes at least one time of water quantity judgment, where a current shale gas exploiting intensity is continued under a circumstance that the water quantity does not exceed a water safety threshold; and monitoring data is transferred to the information system2under a circumstance that the water quantity exceeds the water safety threshold;

the information system2is further used for collecting a gas production rate and a water-deficient value in a current shale gas exploiting stage and transferring the collected data to the decision making system3; the optimum gas production rate in a next stage, water demands of various water sources in the next stage and the water safety threshold in the next stage are obtained in the decision making system3based on the data collected by the information system2, and the decision making system3is used for realizing prediction of the optimum gas production rate in the next stage; and

the regulator4is used for regulating and controlling the shale gas exploiting intensity in the current stage.

The cascade water recycling system includes judging the first time water quality, judging whether the third time water quantity is enough or not, judging whether the third time water quantity is used for shale gas exploitation by 100% or not and judging the first time water quantity exceeds the water safety threshold or not.

As shown inFIG.2, specifically, the waste liquor of the flow back liquid in certain time shale gas exploitation can be purified via conventional physical, chemical and biological process methods for waste liquor. At the same time, in the shale gas exploitation areas, natural rainfall can be collected by the rainwater collection system, and the collected rainwater can be subjected to water quality treatment via the rainwater treatment pool. A shale gas exploiting effluent subjected to water treatment is mixed with rainwater treated by a rainwater treatment pool, a mixed liquid serving as a class I water source for water cycling of shale gas exploitation; a recovered water quantity of such water can be predicted by a water quantity predictor; after the recovered water quantity is determined, whether the first time water quality reaches a standard or not is judged; and if the water quality does not reach the standard, the mixed liquid is subjected to water treatment again, and if the water quality reaches the standard, whether the first time water quantity is enough or not is judged.

The water quantity predictor and the intelligent regulator perform water quantity prediction and regulation and control based on a production standard in the shale gas exploitation industry. The water quantity predictor and the intelligent regulator can be programmable logic controllers (PLC) which are digit arithmetic operation electronic systems designed especially for being applied in an industry environment with functions of controlling sequences, timing, counting and the like and capable of controlling various mechanical and production processes, the models of the PLC being optionally Siemens PLC S7-200.

It is needed to judge whether the first time water quantity is enough or not for the class I water resource with the water quantity reaching the standard. If the water quantity is enough, whether the first time water quantity is utilized by 100% or not is judged; if the water quantity is utilized by 100%, then such water is used for shale gas exploitation and if the water quantity is not utilized by 100%, the excessive water is reinjected to the underground water; and if the water quantity is not enough, it is needed to introduce the class II water source.

It is needed to mix the class I water source which is not enough in water quantity with the class II water source and then judge whether the second time water quantity is enough or not. The class II water source is originated from purchased water, the water quantity of which can be predicted via the purchased water quantity predictor. If the water quantity is enough, whether the second time water quantity is utilized by 100% or not is judged; if the water quantity is utilized by 100%, then such water is used for shale gas exploitation and if the water quantity is not utilized by 100%, the excessive water is reinjected to the underground water; and if the water quantity is not enough, it is needed to introduce the class III water source.

It is needed to mix the class I water source and the class II water source which are not enough in water quantity with the class III water source and then judge whether the third time water quantity is enough or not. The class III water source is originated from surface water and underground water, the water quantity of which can be predicted via the purchased water quantity predictor by means of water quantity. If the water quantity is enough, whether the third time water quantity is utilized by 100% or not is judged; if the water quantity is utilized by 100%, then such water is used for shale gas exploitation and if the water quantity is not utilized by 100%, the excessive water is reinjected to the underground water; and if the water quantity is not enough, it is needed to judge the water safety threshold.

Respective taking quantities of the surface water and the underground water in the class III water source are optimally distributed via an optimal distribution system; as shown inFIG.3, distribution objects of the optimal distribution system are an underground water consumption and a surface water consumption, a policy adopted for distribution is that the utilizable quantity of the surface water and the utilizable quantity of the underground water meet a water demand for shale gas exploitation, parameters of a game payoff function are specified according to the distribution policy, and finally, an optimal distribution model is solved. If the solved model meets a distribution objective, the model is imported into the optimal distribution system for optimal distribution of the class III water source; and if the solved optimal distribution model does not meet the distribution objective, the parameters are reset and the model is solved again.

As shown inFIG.2, whether the water quantity exceeds the water safety threshold or not is judged after the class I water source, the class II water source and the class III water source are mixed. If the water quantity does not exceed the safety threshold, the current shale gas exploiting intensity is continued; and if the water quantity exceeds the safety threshold, the gas production rate in the current stage, a proportion of the class I water source, a proportion of the class II water source and the water-deficient value in the current exploitation process of the shale gas are collected by the information system these data is input into the information system and the decision making system, where the information system can store data efficiently and safely, and needs to fit mass data scenes such as Internet of things and big data, and a Tencent cloud time sequence database-TencentDB for CTSDB can be selected herein.

Preferably, the decision making system is the literature mining intelligent decision making system based on knowledge graph.

The literature mining intelligent decision making system based on knowledge graph constructs a large-scale semantic network which becomes a carrier of big data, then knowledge reasoning is performed by a knowledge reasoning algorithm to look for the relation between the knowledge nodes, which is the characteristic not belonging to other intelligent algorithms. The knowledge graph makes intelligence of the decision making system be superior to that of conventional intelligent systems.

The literature mining intelligent decision making system based on knowledge graph can update the knowledge triad extraction mode according to feedback in the production process of the shale gas exploitation process so as to update the knowledge graph intelligent decision making model, and features real-time and variability, and thereby, its application range is widened.

As shown inFIG.1, based on the data collected by the information system, the optimum gas output in the next stage, the water demands of all classes of water sources in the next stage and the water safety thread in the next stage can be obtained based on the literature mining intelligent decision making system based on knowledge graph.

A specific process of the literature mining intelligent decision making system based on knowledge graph is as shown inFIG.4.

The schematic diagram ofFIG.4illustrates a non-restrictive example of the literature mining intelligent decision making system based on knowledge graph according to the present invention.

Information of the gas output in the current stage, the proportions of all classes of water source and the water-deficient value input by the information system is input into the knowledge graph decision making model constructed in the decision making system, and is researched via the semantic network in the decision making model to output the water demand of the class I water source, the water demand of the class II water source, the water demand of the class III water source, the water safety threshold and the gas output in the next stage. First time judgment is performed after an output result is obtained. If the water demand obtained by decision making meets the current water-deficient value, then it is used for allocation of all classes of water in the next stage, and if the water demand obtained by decision making does not meet the current water-deficient value, then a decision on the water demands of all classes of water is made by utilizing the decision making model again. Then second time judgment is performed. If in the actual allocation process, the water quantity is enough, the decision making model in the previous stage is followed, and if the water quantity is not enough, then knowledge triad extraction is updated, so that the decision making model is updated.

Literatures constructed by the knowledge graph are originated from Chinese periodical databases, patent databases and research reports.

The knowledge triad extraction modes include any n extraction modes. A plurality of non-restrictive examples can be listed with respect to structural and unstructured data in the literatures.

The extraction mode1can be used for extracting the knowledge triads related to the water demand of the class I water source, the extraction mode2can be used for extracting the knowledge triads related to the water demand of the class II water source, and the extraction mode n can be used for extracting the knowledge triads with respect to the relation between the water demand and the water safety threshold by parity of reasoning.

The knowledge graph intelligent decision making model includes any n prediction models which are obtained by constructing the knowledge graph by different knowledge extraction results, and thereby, a plurality of non-restrictive examples can be listed.

The extraction result1is used for constructing the knowledge graph intelligent decision making model1, the extraction result2is used for constructing the knowledge graph intelligent decision making model2, and the extraction result n is used for constructing the knowledge graph intelligent decision making model n by parity of reasoning.

Constructing the knowledge graph intelligent decision making model includes the following steps:

1) engineering data of water resource allocation for shale gas exploitation is originated from a Chinese databases, patent databases and research reports and features wide source and poor structural property, and therefore, knowledge nodes and relation sides can be constructed via a knowledge graph construction technique after text data is pre-processed, so that a knowledge graph intelligent decision making model is constructed, the model aiming to dig out relations among different knowledge nodes from the text data;

2) from the prospective of water resource allocation, an engineering name (a certain shale gas exploitation area), an allocation time, an allocated water resource class (class I, II and III water) and the like can be selected as the knowledge nodes of the knowledge graph model; meanwhile, the water demand of a certain class of water, the shale gas exploiting intensity and the like can further be taken as the knowledge nodes; and finally, “the time is”, “the water demand is” and “include” and the like can be taken as the relation sides among the nodes; and the constructed knowledge graph intelligent decision making model can realize a decision making function on the water demand and the like;

3) a schematic diagram of the knowledge graph model can be listed according to the defined knowledge nodes and relation sides, and a local schematic diagram is as shown inFIG.5; a semantic network graph of the knowledge graph can be expanded to thousands of or even millions of knowledge triads according to the quantity of the triads; and

4) related knowledge reasoning engineering can be performed according to the semantic network graph of the knowledge graph, and one entity predicts and reasons a next entity as well as looks for the relation therebetween, thereby, the knowledge graph intelligent decision making model is constructed.

When knowledge reasoning is performed, a reasoning algorithm: Path Ranking Algorithm based on the knowledge graph structure can be adopted, thereby performing link prediction reasoning by taking a relation side between the knowledge nodes.

A knowledge triad is defined, a knowledge head node being h, the relation side being r and a knowledge end node being t, thereby determining the knowledge triad (h, r, t).

In the PRA algorithm, some path characteristics are generated first by supervised random walk, a path consisting of a series of knowledge nodes and relation sides as follows:

P=T0→r1T1→r2…→rn-1Tn-1→rnTn,(1)

where Tnis a range of the relation side m and a domain of the relation side rn−1, namely, Tn=range (rn)=domain (rn−1). The algorithm defines distribution of one relation side and knowledge node, based on a fact that a value obtained by distribution is a characteristic value Xh, p(t)of each walk path, Xh, p(t)can be construed as a probability from the knowledge head node n to the knowledge end node t along a certain path p. An updating rule of the Xh, p(t)is as follows:

Xh,p⁡(e)=∑e′∈range⁡(p′)Xh,p′(e′)×P⁡(e❘e′;rl),(2)

where if e=S (the knowledge node in the path p), Xh, p (e)=1 and otherwise, Xh, p

(e)=0.P(e❘e′;rl)=rl(e′,e)❘"\[LeftBracketingBar]"rl(e′,·)❘"\[RightBracketingBar]"
represents a probability of starting from a knowledge node e′ and reaching a knowledge node e along a relation side r1. r1(e′, e) represents whether there is a path with the relation type of r1between the knowledge node e′ and the knowledge node e, if yes, a value thereof is 1, and otherwise, a value thereof is 0, and |r1(e′, ⋅)| represents a quantity of the knowledge nodes reachable from the knowledge node e′ through the relation side at a regulated path.

If it is desired to determine the relation side r between certain two knowledge nodes, it is necessary to obtain to a group of characteristic path Pr=(P1, . . . Pn) by the supervised random walk, the supervised random walk refers to guiding the walking knowledge nodes to walk based on random walk in a supervising mode, so that the random walk is purposeful. In the example, the knowledge nodes and the relation sides related to the water demand are searched purposefully by the supervised random walk. A sequencing model for a prediction entity is then trained by utilizing these characteristic paths, and the sequencing model can be modeled by adopting a linear model method:

Si=f⁡(h,ri,t)=∑p∈prXh,p(t)⁢θp,(3)

where f(h,ri,t) represents a likelihood Sithat there is a relation ribetween the knowledge node h and the knowledge node t, θprepresents a weight factor of the characteristic path corresponding to Pr, and a value of θpcan be solved by training. yi={0, 1} can be used to represent a value of a certain training sample; if the value is 1, it is represented that there is the relation side r between the two knowledge nodes, and if the value is 0, it is represented that there is no relation side r. Under a common circumstance, a sigmoid function can be used to map a predicted result to an interval of [0, 1], with a specific form as follows:

P⁡(yi=1❘Si)=exp⁡(Si)1+exp⁡(Si),(4)

with respect to the weight factor θp, a loss function can be designed through the following linear changes in addition to maximum likelihood estimation:

L⁡(θ)=∏i=1nPiyi(1-Pi)1-yi,(5)ln⁢L⁡(θ)=∑i=1n(yi⁢ln⁢Pi+(1-yi)⁢ln⁡(1-Pi)).(6)

Finally, the intelligent decision making model based on the semantic network of the knowledge graph can be converted into an optimized target function with respect to the optimized weight factor θp.

The knowledge graph intelligent decision making model is evaluated after the knowledge graph intelligent decision making model is initially constructed; if an evaluation result indicates that the decision making model can be used for making decisions, it can be input by the information system to obtain an output value such as the water demand; and

if the evaluation result indicates that the decision making model cannot be used for making decisions, the knowledge triad is updated and extracted, so that the intelligent decision making model is updated.

Input of the knowledge graph intelligent decision making model is prediction of the water demands of all classes of water resource, and the prediction is a ground of the supervised random walk. According to the prediction, search and decision making can be performed in the knowledge graph intelligent decision making model to look for the optimum water demand output result, and a non-restrictive example for prediction of the water demand at the output end is limited below.

It is assumed that with a certain period of time, the water demands of certain class of water resource from a time1to a time t are respectively Q1, Q2. . . Qt, and the water demand Q(t+1)of the certain class of water resource at the time (t+1) can be predicted via the water demand prediction model by using the following prediction model.

A water demand change value at the time t:
ΔQt=Qt−Qt−1(7).

A second order difference water demand change value at the time t:
Δ2Qt=ΔQt−ΔQt−1(8).

A second order difference water demand change predicted value at the time t:
Δ2{circumflex over (Q)}t+1=αΔ2Qt+(1−α)Δ2{circumflex over (Q)}t(9).

Thus, a water demand predicted value at the time (t+1):
{circumflex over (Q)}t+1=Δ2{circumflex over (Q)}t+1+ΔQt+Qt(10).

A value of {circumflex over (Q)}t+1is input, and searching is performed via the constructed knowledge graph intelligent decision making policy to output the corresponding water demand values.

As shown inFIG.2, regulation and control by the regulator is based on output of the gas output in the next stage, the water demand in the next stage and the water safety threshold in the next stage by the decision making system. The current shale gas exploiting intensity and the water demand can be regulated via the regulator according to a specific output result of the decision making system by way of reducing the output of the waste liquor and increasing the water consumption of rainwater.