A method for predicting pore pressure of formation to be drilled ahead of the drill bit based on grey prediction theory

Abstract

The present invention discloses a method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory, applies the grey prediction theory to predict the pore pressure of the formation to be drilled ahead of the drill bit during the drilling process, and constructs a pressure prediction model. This solution can provide on-site drilling technicians with relatively accurate pore pressure information of the formation to be drilled ahead of the drill bit, and can perform a dynamic risk assessment of the drilling operation process based on the prediction result of the pore pressure of the formation to be drilled, and then optimize and adjust the drilling construction measures according to the risk assessment result, thereby assisting the drilling technicians to make quick and accurate decisions and prevent the drilling risk from further deteriorating, and finally reduce the operational risk caused by inaccurate understanding of the pore pressure information, so as to achieve the goal of reducing the drilling risk or risk-free drilling.

Description

Technical Field

[0001] The invention relates to the technical field of deep well drilling, and in particular to a method for predicting the pore pressure of a formation to be drilled ahead of a drill bit based on grey prediction theory. Background Art

[0002] Formation pore pressure is the basic data reflecting the fluid conditions in the formation, rock types and their engineering mechanical properties, as well as geological structures. Accurate prediction of formation pore pressure is an important prerequisite for ensuring smooth and safe drilling from design to construction. Therefore, formation pore pressure monitoring and prediction has always been an important task in oil and gas drilling. At present, the methods for obtaining abnormal formation pore pressure are mainly divided into the following categories: pre-drilling pressure prediction, while-drilling pressure monitoring, geophysical logging pressure detection, and pressure measurement. Among them, geophysical logging is generally recognized as an important means for accurately predicting formation pore pressure. However, this method is a post-prediction method, which cannot predict the pore pressure of the formation that has not been drilled below the bottom of the well. Neither the while-drilling monitoring method nor the post-drilling logging pressure detection method can predict the pressure value of the formation to be drilled in front of the drill bit. Pre-drilling pressure prediction mainly uses seismic layer velocity data and its relationship model with formation pore pressure to calculate formation pore pressure. Commonly used methods include equivalent depth method, single-point prediction model and comprehensive prediction model. Pressure monitoring using various drilling and logging parameters during drilling has been widely used in the actual drilling process of oil and gas fields, and has played a role in real-time guidance of drilling projects.

[0003] However, the pre-drilling prediction method of formation pore pressure uses seismic data to predict formation pore pressure, while the underground geological conditions are relatively complex, and there is too little information below the bottom of the well, or there is no useful information at all. Therefore, the pre-drilling prediction of formation pore pressure is more difficult, so that the results predicted by the pre-drilling prediction method of formation pore pressure are inaccurate. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method for predicting the pore pressure of the formation to be drilled in front of the drill bit based on grey prediction theory. By using a formation pore pressure prediction model established based on grey prediction theory, the formation pore pressure of the area to be drilled is predicted, and more accurate formation pore pressure information of the formation to be drilled below the drill bit is provided to on-site drilling operators, thereby solving the risks caused by inaccurate understanding of formation pore pressure information and achieving the purpose of risk-free drilling.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A method for predicting pore pressure of a formation to be drilled ahead of a drill bit based on grey prediction theory, comprising:

[0007] Step 1: Based on the pre-drilling data, obtain n formation pressures while drilling of the drill bit in the preset well depth range of the target well, and determine the original sequence p corresponding to the n formation pressures while drilling (0) ;

[0008] Step 2: For the original sequence p (0) Preprocessing is performed to obtain p' (0) ;

[0009] Step 3: For p' (0) Establishing a formation pore pressure prediction model of the target well based on grey theory;

[0010] Step 4: Predicting the formation pore pressure of the to-be-drilled area of ​​the target well based on the formation pore pressure prediction model.

[0011] Preferably, based on the pre-drilling data, n formation pressures while drilling of the drill bit in a preset well depth interval of the target well are obtained, and the original sequence p corresponding to the n formation pressures while drilling is determined. (0) ,include:

[0012] Based on the pre-drilling data, n formation pressures while drilling of the drill bit in the preset well depth range of the target well are obtained, and the n formation pressures while drilling are sorted in ascending order according to the well depth to obtain the original sequence p (0) ={p (0) (k)}={p (0) (1),p (0) (2),...,p (0) (n)},k=1,2,...,n.

[0013] Preferably, in step 2, the original sequence p (0) Preprocessing to get p' (0) ,include:

[0014] The original sequence p is calculated by moving average method. (0) Preprocessing to obtain the processed original sequence p' (0) = {p' (0) (k)}={p' (0) (1),p' (0) (2),...,p' (0) (n)},k=1,2,...,n.

[0015] Preferably, in step 3, based on the processed original sequence p' (0) and the pre-established grey model to obtain the formation pore pressure prediction model of the target well, including: constructing a grey model GM(1,1), solving the reduction model and performing a residual test.

[0016] Preferably, the construction of the grey model GM(1,1) comprises:

[0017] The processed original sequence p' (0) Perform first-order accumulation to obtain the generated sequence:

[0018] p (1) ={p (1) (k)}, k = 1, 2, ..., n;

[0019] For the sequence p (1) After mean processing, we get:

[0020] z (1) ={z (1) (k)}={z (1) (1),z (1) (2),...,z (1) (n)}, k = 1, 2, ..., n;

[0021] Among them, z (1) (k) = 0.5[p (1) (k)+p (1) (k-1)].k=2,...,n;

[0022] Based on the pre-established grey differential equation Calculate coefficient a and coefficient b;

[0023] Substitute the coefficients a and b into the grey differential equation, and use the whitening response function of the grey differential equation to calculate the original sequence p (0) The simulated value of

[0024] Determined based on the least squares method

[0025] Preferably, the solving and restoring model comprises: Do first-order accumulation to generate the original sequence p (0) The simulated value of A formation pore pressure prediction model of the target well is obtained.

[0026] Preferably, the performing residual test comprises:

[0027] Based on the preset residual value Δ(k) and the residual relative value ε(k), Calculate the average precision q;

[0028] Determine whether the average precision q is less than a preset average precision;

[0029] If the average precision q is not less than the preset average precision, proceed to step 4;

[0030] If the average precision q is less than the preset average precision, steps 2 to 3 are repeated.

[0031] Preferably, in step 4, predicting the formation pore pressure of the to-be-drilled area of ​​the target well based on the formation pore pressure prediction model includes:

[0032] Predicting the formation pore pressure of the formation to be drilled ahead of the drill bit based on the formation pore pressure prediction model

[0033] Preferably, after executing step 4, the method further includes:

[0034] Remove the formation pore pressure at the first point above the drill bit and convert the predicted pressure value Add to the original sequence and update the original data of formation pressure while drilling:

[0035] From the above content, it can be seen that the present invention discloses a method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory, obtaining n while-drilling formation pressures of the drill bit in the preset well depth interval of the target well based on pre-drilling data, and determining the original number series p corresponding to the n while-drilling formation pressures (0) ; For the original sequence p (0) Preprocessing is performed to obtain p' (0) ; For p' (0) A formation pore pressure prediction model of the target well is established based on grey theory; and the formation pore pressure of an undrilled area of ​​the target well is predicted based on the formation pore pressure prediction model. Through the above-disclosed method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory, the pressure monitoring results of the drilled formation within a certain depth range above the drill bit position are obtained in the target well as the initial raw data, and the moving sliding average method is used to preprocess the original sequence to reduce its randomness and uncertainty, thereby obtaining a new sequence that is easier to model, and then the differential dynamic equation of the system is constructed by fitting, and the model built according to the new sequence is restored and generated to establish a formation pore pressure prediction model, and the formation pore pressure model of the area to be drilled is predicted by the established formation pore pressure prediction model, and more accurate formation pore pressure information of the formation to be drilled below the drill bit is provided to the on-site drilling operators, and dynamic engineering risk assessment can be performed based on the formation pore pressure prediction results of the formation to be drilled, and then the drilling construction measures are optimized and adjusted according to the risk assessment results, thereby assisting the drilling operators to make quick and accurate decisions and prevent the drilling risk from further expanding, and finally reducing the risks caused by inaccurate understanding of formation pore pressure information, so as to achieve the goal of risk-free drilling. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for predicting pore pressure of a formation to be drilled ahead of a drill bit based on grey prediction theory provided in an embodiment of the present invention;

[0038] Figure 2 A flow chart for predicting the formation pore pressure of the formation to be drilled ahead of the drill bit based on the grey theory provided in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of predicting the formation pore pressure of the formation to be drilled ahead of the drill bit based on the grey theory provided in an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of the variation of acoustic time difference while drilling with depth of the XX well provided by an embodiment of the present invention;

[0041] Figure 5 A schematic diagram of the variation of resistivity while drilling with depth of the XX well provided by an embodiment of the present invention;

[0042] Figure 6 A schematic diagram of the variation of formation pressure with depth while drilling in a XX well provided by an embodiment of the present invention;

[0043] Figure 7 A flow chart for predicting the pore pressure of the formation to be drilled 10 m ahead of the drill bit at a depth of 1750 m in the XX well provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] In this application, the terms "comprises", "comprising" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element.

[0046] The embodiment of the present invention provides a method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory. Figure 1 , is a schematic diagram of the process of predicting the pore pressure of the formation to be drilled ahead of the drill bit in the embodiment of the present application. The method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory at least comprises the following steps:

[0047] Step 1: Based on the pre-drilling data, obtain n formation pressures while drilling of the drill bit in the preset well depth range of the target well, and determine the original sequence p corresponding to the n formation pressures while drilling (0) .

[0048] In step 1, the pre-drilling data include well logging data such as sonic wave, density, natural gamma, etc., test analysis data, etc. of the drilled well.

[0049] It should be noted that the preset well depth interval is the well section drilled above the drill bit during the drilling process. In this well section, n formation pressures while drilling in this well section are monitored through pre-drilling data.

[0050] To achieve the above purpose, we can first take the well depth position at the drill bit as the origin, then take the formation pressure while drilling at n points equidistantly upward, and finally arrange the obtained formation pressure while drilling from shallow to deep into a series as the original series p (0) .

[0051] Step 2: For the original sequence p (0) Preprocessing is performed to obtain p' (0) .

[0052] In step 2, the preprocessing is to process the original sequence by a moving average method.

[0053] It should be noted that the moving average method is a simple smoothing forecasting technology. Its basic idea is to calculate the average time sequence of a certain number of items one by one based on the time series data to reflect the long-term trend. Therefore, when the value of the time series fluctuates greatly due to the influence of periodic changes and random fluctuations, and it is difficult to show the development trend of the event, the moving average method can eliminate the influence of these factors, show the development direction and trend of the event (i.e., trend line), and then analyze and predict the long-term trend of the series based on the trend line.

[0054] It should also be noted that the original sequence p is calculated by moving average method. (0) Preprocessing can get the processed original sequence p' (0) = {p' (0) (k)}={p' (0) (1),p' (0) (2),...,p' (0) (n)},k=1,2,...,n.

[0055] in,

[0056] Step 3: For p' (0) A formation pore pressure prediction model for the target well is established based on grey theory.

[0057] In step 3, the grey theory is an applied mathematics discipline that studies information that is partially clear, partially unclear, and has uncertainty. Grey system theory was first proposed by Chinese scholar Professor Deng Julong of Huazhong University of Science and Technology in 1982, and has subsequently received attention and attention at home and abroad. "Poor information, small sample system" is the main research content of grey system theory. It processes, mines and utilizes the existing "poor information" and correctly grasps and describes the evolution laws of system operation behavior, thereby realizing the understanding of the real world and predicting future states. The scope of application of grey system theory has been expanded to many scientific fields such as industry, agriculture, society, economy, energy, geology, and petroleum. It has successfully solved a large number of practical problems in production, life, and scientific research, and achieved remarkable results.

[0058] It should be noted that p' (0) The formation pore pressure prediction model of the target well established based on the grey theory is a model that can predict the formation pore pressure of the formation to be drilled in the target well.

[0059] In the specific implementation of step 3, the formation pore pressure prediction model of the target well can be obtained by constructing the grey model GM(1,1), solving the reduction model and performing residual test.

[0060] Step 4: Predicting the formation pore pressure of the to-be-drilled area of ​​the target well based on the formation pore pressure prediction model.

[0061] In step 4, the formation pore pressure of the target well in the area to be drilled can be predicted by the formation pore pressure prediction model.

[0062] The embodiment of the present application obtains n while-drilling formation pressures of the drill bit in a preset well depth interval of the target well based on pre-drilling data, and determines the original sequence p corresponding to the n while-drilling formation pressures. (0) ; For the original sequence p (0) Preprocessing is performed to obtain p' (0) ; For p' (0) A formation pore pressure prediction model of the target well is established based on grey theory; and the formation pore pressure of an undrilled area of ​​the target well is predicted based on the formation pore pressure prediction model. Through the above-disclosed method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on the grey prediction theory, the pressure monitoring results of the drilled formation within a certain depth range above the drill bit position are obtained in the target well as the initial raw data, and the moving sliding average method is used to preprocess the original sequence to reduce its randomness and uncertainty, thereby obtaining a new sequence that is easier to model, and then the differential dynamic equation of the system is constructed by fitting, and the model built according to the new sequence is restored and generated to establish a formation pore pressure prediction model, and the formation pore pressure model of the area to be drilled is predicted by the established formation pore pressure prediction model, and more accurate formation pore pressure information of the formation to be drilled below the drill bit is provided to the on-site drilling operators, and dynamic engineering risk assessment can be performed based on the formation pore pressure prediction results of the formation to be drilled, and then the drilling construction measures are optimized and adjusted according to the risk assessment results, thereby assisting the drilling operators to make quick and accurate decisions and prevent the drilling risk from further expanding, and finally reducing the risks caused by inaccurate understanding of formation pore pressure information, so as to achieve the goal of risk-free drilling.

[0063] Preferably, in step 3, based on the processed original sequence p' (0) and the pre-established grey model to obtain a formation pore pressure prediction model for the target well, comprising the following steps:

[0064] Firstly, the grey model GM(1,1) is constructed, then the reduction model is solved, and finally the residual test is performed.

[0065] It should be noted that the G of the grey model GM(1,1) stands for grey, M stands for model, GM(1,1) represents a model of order 1 and 1 variable, and the residual in mathematical statistics refers to the difference between the actual observed value and the estimated value (fitted value). The "residual" contains important information about the basic assumptions of the model. If the regression model is correct, we can regard the residual as the observed value of the error.

[0066] Preferably, the construction of the grey model GM(1,1) comprises:

[0067] The processed original sequence p' (0) Perform first-order accumulation to obtain the generated sequence:

[0068] p (1) ={p (1) (k)}, k = 1, 2, ..., n;

[0069] For the sequence p (1) After mean processing, we get:

[0070] z (1) ={z (1) (k)}={z (1) (1),z (1) (2),...,z (1) (n)}, k = 1, 2, ..., n;

[0071] Among them, z (1) (k) = 0.5[p (1) (k)+p (1) (k-1)].k=2,...,n;

[0072] Based on the pre-established grey differential equation Calculate coefficient a and coefficient b;

[0073] Substitute the coefficients a and b into the grey differential equation, and use the whitening response function of the grey differential equation to calculate the original sequence p (0) The simulated value of

[0074] Determined based on the least squares method

[0075] Preferably, the solving and restoring model comprises: Do first-order accumulation to generate the original sequence p (0) The simulated value of A formation pore pressure prediction model of the target well is obtained.

[0076] In the specific implementation process, you can first Do a first-order accumulation to get the restored model:

[0077]

[0078]

[0079] When k=1,2,...,n-1, we can get the original sequence p' (0) The simulated value of

[0080] Preferably, the performing residual test comprises:

[0081] Based on the preset residual value Δ(k) and the residual relative value ε(k), Calculate the average precision q;

[0082] Determine whether the average precision q is less than a preset average precision;

[0083] If the average precision q is not less than the preset average precision, proceed to step 4;

[0084] If the average precision q is less than the preset average precision, steps 2 to 3 are repeated.

[0085] In the specific implementation, Δ(k) can be set as the residual value, ε(k) as the relative value of the residual, and q as the average accuracy:

[0086] but

[0087] Derived

[0088] If the average accuracy q ≥ 90%, it means that the sequence meets the requirements of modeling and can be predicted; otherwise, the modeling process is repeated until the average accuracy q meets the conditions before proceeding to the next step.

[0089] It should be noted that by testing the residuals of the formation pore pressure prediction model, the accuracy of the prediction model in predicting the formation pore pressure can be effectively improved, and more accurate pore pressure information of the formation to be drilled in front of the drill bit can be provided to on-site drilling technicians. Based on the prediction results of the pore pressure of the formation to be drilled, a dynamic risk assessment of the drilling operation process can be performed, and then the drilling construction measures can be optimized and adjusted according to the risk assessment results, thereby assisting drilling technicians to make quick and accurate decisions and prevent the drilling risk from further deteriorating, and ultimately reducing the operational risks caused by inaccurate understanding of pore pressure information, so as to achieve the goal of reducing drilling risks or risk-free drilling.

[0090] Preferably, in step 4, predicting the formation pore pressure of the undrilled area of ​​the target well based on the formation pore pressure prediction model includes:

[0091] Predicting the formation pore pressure of the formation to be drilled ahead of the drill bit based on the formation pore pressure prediction model Preferably, after executing step 4, the method further includes:

[0092] Remove the formation pressure monitoring data at the first point above the drill bit and convert the predicted pressure value Add to the original sequence and update the original formation pressure data to:

[0093]

[0094] It should be noted that, during the drilling process of the drill bit in the target well in the area to be drilled, the new original series of formation pore pressure p *(0) Restarting the formation pore pressure prediction for the next adjacent point and re-executing steps 1 to 4 can provide the on-site drilling technicians with more accurate pore pressure information of the formation to be drilled in front of the drill bit. Based on the pore pressure prediction results of the formation to be drilled, a dynamic risk assessment of the drilling operation process can be performed, and then the drilling construction measures can be optimized and adjusted according to the risk assessment results, thereby assisting the drilling technicians to make quick and accurate decisions and prevent the drilling risk from further deteriorating, and ultimately reducing the operational risks caused by inaccurate understanding of pore pressure information, so as to achieve the goal of reducing drilling risks or risk-free drilling.

[0095] According to the method disclosed in the above embodiment, the present solution is further introduced below in combination with specific embodiments: Summary of the invention:

[0097] The present invention proposes a method for predicting the pore pressure of the formation to be drilled in front of the drill bit based on the grey prediction theory, which can provide on-site drilling operators with relatively accurate pore pressure information of the formation to be drilled below the drill bit. Based on the pressure prediction results of the formation to be drilled, a dynamic engineering risk assessment can be performed, and then the drilling construction measures can be optimized and adjusted according to the risk assessment results, thereby assisting the drilling operators to make quick and accurate decisions and preventing the drilling risk from further expanding, and ultimately reducing the risks caused by inaccurate understanding of the pressure information of the formation to be drilled, so as to achieve the goal of risk-free drilling.

[0098] Technical solution:

[0099] First, collect seismic layer velocity processing data, well logging data such as acoustic wave, density, natural gamma, etc., test analysis data, etc., and cooperate with core testing to obtain the vertical formation rock mechanical parameters of the area; comprehensively consider the complexity of the geological environment, the ambiguity of well logging seismic interpretation data, and the accuracy of the pressure prediction model, and predict the formation pore pressure profile;

[0100] Then, a formation pore pressure prediction model based on grey theory was established, and the pore pressure of the formation to be drilled in front of the drill bit was predicted according to the pressure monitoring results of the drilled section above the drill bit. The pressure monitoring results of the drilled formation within a certain depth range above the drill bit position were selected as the initial raw data, and the moving average method was used to pre-process the original sequence to reduce its randomness and uncertainty, so as to obtain a new sequence that is easier to model. Then, the differential dynamic equation of the system was constructed by fitting, and the model built according to the new sequence was restored and generated, and finally the pressure prediction model was established.

[0101] The method is mainly divided into the following steps: (1) constructing the original sequence; (2) preprocessing the original sequence; (3) constructing the grey model GM(1,1); (4) solving the reduction model; (5) performing residual test; (6) predicting the formation pore pressure while drilling.

[0102] Steps in detail:

[0103] Grey system theory was first proposed by Professor Deng Julong of Huazhong University of Science and Technology in 1982, and has subsequently attracted attention and attention at home and abroad. "Poor information, small sample system" is the main research content of grey system theory. It processes, mines and utilizes the existing "poor information" and correctly grasps and describes the evolution law of system operation behavior, so as to understand the real world and predict future states. The application scope of grey system theory has been expanded to many scientific fields such as industry, agriculture, society, economy, energy, geology, and petroleum, and has successfully solved a large number of practical problems in production, life and scientific research, and achieved remarkable results.

[0104] The present invention establishes a formation pore pressure prediction model based on grey theory, and predicts the pore pressure of the formation to be drilled in front of the drill bit according to the pressure monitoring results of the drilled section above the drill bit. The pressure monitoring results of the drilled formation within a certain depth range above the drill bit position are selected as the initial raw data, and the moving sliding average method is used to pre-process the original sequence to reduce its randomness and uncertainty, thereby obtaining a new sequence that is easier to model. The differential dynamic equation of the system is then constructed by fitting, and the model built according to the new sequence is restored and generated, and finally a pressure prediction model is established. The steps are as follows:

[0105] (1) Constructing the original sequence

[0106] Taking the well depth position at the drill bit as the origin, take the formation pressure monitoring values ​​of n points equidistantly upward and arrange them into a series from shallow to deep as the original data:

[0107] p (0) ={p (0) (k)}={p (0) (1),p (0) (2),...,p(0) (n)},k=1,2,...,n. (1)

[0108] (2) Preprocessing the original sequence

[0109] Apply the moving average method to preprocess the original series:

[0110] p' (0) = {p' (0) (k)}={p' (0) (1),p' (0) (2),...,p' (0) (n)},k=1,2,...,n. (2)

[0111]

[0112] (3) Constructing the grey model GM(1,1)

[0113] For a given p' (0) , do 1-AGO (first-order accumulation) to get the generated sequence p (1) ={p (1) (k)}, k = 1, 2, ..., n. Where: For the newly generated sequence p (1) Perform mean processing to obtain z (1) ={z (1) (k)}={z (1) (1),z (1) (2),...,z (1) (n)},k=1,2,...,n.

[0114] Among them, z (1) (k) = 0.5[p (1) (k)+p (1) (k-1)].k=2,...,n.

[0115] Establish the grey differential equation:

[0116]

[0117] Using the grey differential equation, the coefficients a and b are calculated:

[0118] p (1) (k) = az (1) (k) = b (5)

[0119] Substituting the coefficients into the grey differential equation, the whitening response function of the equation can be used to calculate the simulated value of the original series; solving the equation using the least squares method, the solution is:

[0120]

[0121] (4) Solving the reduction model

[0122] right Make 1-IAGO to get the reduced model:

[0123]

[0124] When k=1,2,...,n-1, we can get the original sequence p' (0) The simulated value of

[0125] (5) Perform residual test

[0126] Let Δ(k) be the residual value, ε(k) be the relative value of the residual, and q be the average accuracy:

[0127]

[0128]

[0129] If the average accuracy q ≥ 90%, it means that the sequence meets the modeling requirements and can be predicted; otherwise, repeat steps (1) to (5) until the conditions are met before proceeding to the next step.

[0130] (6) Formation pore pressure prediction while drilling

[0131] When the accuracy meets the requirements, the pore pressure of the formation to be drilled ahead of the drill bit is predicted:

[0132]

[0133] Remove the formation pore pressure monitoring data of the first point at the top and take the predicted pressure value Add to the original sequence and update the original data of formation pore pressure:

[0134]

[0135] Using the new formation pore pressure original series p *(0) Restart the formation pore pressure prediction of the next adjacent point, and follow the same steps as above. The flow chart of formation pore pressure prediction ahead of the drill bit based on grey theory is as follows: Figure 2 and Figure 3 shown.

[0136] Examples:

[0137] The present invention takes Well XX as an example for example calculation and result analysis. The pre-drilling pressure prediction results of Well XX show that the pressure coefficient fluctuates between 1.0 and 1.2 before 1500m, which belongs to the normal hydrostatic pressure system; and from below 1500m, the pressure begins to rise gradually. The existence of abnormally high pressure seriously affects the on-site safety of drilling. Therefore, while drilling pressure monitoring is carried out when drilling at a depth of 1500m. Figure 4 , Figure 5 and Figure 6 As shown. Assuming that the drill bit is at a well depth of 1750m at this time, firstly, the formation pore pressure of the well section 1700m to 1749m above the drill bit is calculated based on the well logging data while drilling, and the formation pore pressure of the well section 1750m to 1759m to be drilled in front of the drill bit is predicted using the gray prediction method established in the present invention. The specific steps are as follows Figure 7 shown.

[0138] First, the formation pore pressure monitoring data from 1700m to 1749m are selected as the original sequence, as shown in Table 1. Assume that the original data sequence of formation pore pressure is:

[0139] P (0) ={p (0) (k)}={p (0) (1),p (0) (2),...,p (0) (50)}, k = 1, 2, ..., 50.

[0140] Let the sequence z (1) ={z (1) (k)}={z (1) (1),z (1) (2),...,z (1) (50)}, k = 1, 2, ..., 50. is the sequence generated by the immediate neighbor of P, where z (1) (k) = 0.5[p (1) (k)+p (1) (k-1)].k=2,...,50.

[0141] Table 1: Formation pore pressure monitoring data from 1700m to 1749m in Well XX

[0142]

[0143] The formation pore pressure while drilling prediction model was established by using 50 sets of while drilling formation pressure monitoring data sequences in combination with the grey system theory modeling software (GTMS3.0) and MATLAB software. The first-order nearest neighbor mean generation sequence was performed using the grey system theory modeling software, and the results are shown in Table 2.

[0144] Table 2: First-order neighbor mean generation sequence of formation pore pressure

[0145]

[0146] Substitute the first-order adjacent generation sequence into the smooth sequence judgment condition and it meets the smooth sequence condition. Perform first-order accumulation generation (1-AGO) on the original data sequence, and the results are shown in Table 3.

[0147] Table 3: First-order accumulation generation sequence of formation pore pressure

[0148]

[0149] It can be seen from Table 3 that the first-order cumulative sequence of formation pore pressure is a non-negative increasing sequence and has good smoothness. According to the above two steps, it can be judged that the original data sequence of formation pore pressure has a good smoothness ratio, so it can be substituted into the gray theory modeling software and combined with MATLAB software for modeling and prediction.

[0150] Step 1: Initialize the original formation pore pressure data and list the original formation pore pressure data sequence.

[0151] Step 2: Generate the 1-AGO sequence of the original data sequence, as shown in Table 3.

[0152] Step 3: 1-AGO generates a sequence next to the mean, as shown in Table 4.

[0153] Step 4: Calculate the model coefficients: development coefficient a = 0.0003, gray action b = 1.4672.

[0154] Step 5: Calculate the simulation values ​​of the system characteristic data sequence. The results are shown in Table 5.

[0155] Step 6: Calculate the residual, which is 0.0028; and the relative average error of the formation pore pressure prediction model is: Therefore, the model accuracy is ideal.

[0156] Step 7: Based on the established model, the formation pore pressure in the next ten steps (1750m-1759m before drilling) is predicted and compared with the formation pressure monitoring results while drilling. The results are shown in Table 6.

[0157] Table 4: Sequence of adjacent mean values ​​of formation pore pressure

[0158]

[0159] Table 5: Simulated values ​​of formation pore pressure

[0160] Serial number Analog value Serial number Analog value Serial number Analog value Serial number Analog value Serial number Analog value 1 1.443232 11 1.463266 21 1.45956 31 1.455864 41 1.452178 2 1.466609 12 1.462895 22 1.45919 32 1.455495 42 1.451809 3 1.466237 13 1.462524 23 1.458821 33 1.455126 43 1.451441 4 1.465865 14 1.462153 24 1.45845 34 1.454758 44 1.451074 5 1.465493 15 1.461782 25 1.458081 35 1.454388 45 1.450705 6 1.465123 16 1.461412 26 1.457711 36 1.45402 46 1.450338 7 1.46475 17 1.461042 27 1.457342 37 1.453651 47 1.44997 8 1.464379 18 1.460671 28 1.456972 38 1.453282 48 1.449602 9 1.464008 19 1.4603 29 1.456602 39 1.452914 49 1.449235 10 1.463637 20 1.459931 30 1.456234 40 1.452546 50 1.448867

[0161] Table 6: Error between predicted and monitored formation pore pressure

[0162]

[0163] The formation pore pressure prediction results calculated based on the grey theory for the 1750m-1759m section were compared with the formation pore pressure monitoring results while drilling. It was found that the maximum relative error was 3.408% and the average relative error was 3.038%, indicating that the model has high accuracy and can accurately predict the formation pore pressure within 10m below the drill bit, which can meet the requirements of on-site drilling construction.

[0164] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can refer to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system or system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without creative work.

[0165] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0166] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the pore pressure of the formation to be drilled ahead of the drill bit based on grey prediction theory, characterized in that: include: Step 1: Based on the pre-drilling data, obtain n formation pressures while drilling of the drill bit in the preset well depth range of the target well, and determine the original sequence p corresponding to the n formation pressures while drilling (0) ; Step 2: For the original sequence p (0) Preprocessing is performed to obtain p' (0) ,include: The original sequence p is calculated by moving average method. (0) Preprocessing to obtain the processed original sequence p' (0) = {p' (0) (k)}={p' (0) (1),p' (0) (2),…,p' (0) (n)}, k=1,2,…,n (2); in, Step 3: For p' (0) Establishing a formation pore pressure prediction model of the target well based on grey theory; Step 4: predicting the formation pore pressure of the to-be-drilled area of ​​the target well based on the formation pore pressure prediction model, including: The formation pore pressure of the formation to be drilled ahead of the drill bit is predicted based on the formation pore pressure prediction model: After step 4, the method further includes: Remove the formation pore pressure at the first point above the drill bit and convert the predicted pressure value Add to the original sequence and update the original data of formation pressure while drilling:

2. The method according to claim 1, characterized in that: In step 1, n formation pressures while drilling of the drill bit in a preset depth interval of the target well are obtained based on pre-drilling data, and the original sequence p corresponding to the n formation pressures while drilling is determined. (0) ,include: Based on the pre-drilling data, n formation pressures while drilling of the drill bit in the preset well depth range of the target well are obtained, and the n formation pressures while drilling are sorted in ascending order according to the well depth to obtain the original sequence: (0) ={p (0) (k)}={p (0) (1),p (0) (2),...,p (0) (n)},k=1,2,…,n (1).

3. The method according to claim 1, characterized in that In step 2, based on the processed original sequence p' (0) and the pre-established grey model to obtain the formation pore pressure prediction model of the target well, including: constructing a grey model GM(1,1), solving the reduction model and performing a residual test.

4. The method according to claim 3, characterized in that The construction of the grey model GM(1,1) includes: The processed original sequence p' (0) Perform first-order accumulation to obtain the generated sequence: p (1) ={p (1) (k)},k=1,2,…,n; For the sequence p (1) After mean processing, we get: z (1) ={z (1) (k)}={z (1) (1),z (1) (2),…,z (1) (n)},k=1,2,…,n; Among them, z (1) (k) = 0.5[p (1 )(k)+p( 1) (k-1)].k=2,...,n; Based on the pre-established Grey differential equation: Calculate the coefficient a and coefficient b: p (1) (k)=az (1) (k)=b (5); Substitute the coefficients a and b into the grey differential equation, and use the whitening response function of the grey differential equation to calculate the original sequence p (0) The simulated value of Determined based on the least squares method 5. The method according to claim 4, characterized in that The solution to the restoration model includes: Do first-order accumulation to generate the original sequence p (0) The simulated value of The formation pore pressure prediction model of the target well is obtained: When k=1,2,...,n-1, we can get the original sequence p' (0) The simulated value of 6. The method according to claim 5, characterized in that The residual test comprises: Based on the preset residual value △(k) and the residual relative value ε(k), ε(k) (0) =△(k) (0) / p' (0) (k) Calculate the average precision q: Determine whether the average precision q is less than a preset average precision; If the average precision q is not less than the preset average precision, proceed to step 4; If the average precision q is less than the preset average precision, steps 2 to 3 are repeated.

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