Dynamic optimization method for drilling parameters of high-sulfur-content stratum

Abstract

The invention discloses a dynamic optimization method for drilling parameters of a high-sulfur-content stratum, and relates to the technical field of oil-gas field development. The method comprises the following steps that engineering parameters in the drilling process are obtained in real time; the engineering parameters comprise to-be-optimized drilling parameters and hydrogen sulfide concentration; the hydrogen sulfide escape risk index is calculated in combination with engineering parameters, in the formula, H2Srisk represents the hydrogen sulfide escape risk index, CHS represents the hydrogen sulfide concentration, rho represents the drilling fluid density, G represents the geological risk, and k1, k2 and k3 all represent weights. A dynamic optimization function is established and solved, and when the dynamic optimization function value is minimum, the corresponding drilling parameter combination is the optimal drilling parameter combination. According to the method, the escape risk of hydrogen sulfide can be greatly reduced, the mechanical drilling speed is increased, equipment corrosion and vibration are reduced, safety and efficiency are both considered, and the method adapts to complex working conditions and is suitable for various high-sulfur-content stratum drilling scenes.

Description

Technical Field

[0001] This application relates to the field of oil and gas field development technology, and in particular to a method for dynamic optimization of drilling parameters in high sulfur formations. Background Technology

[0002] The development of high-sulfur gas reservoirs is of great significance for energy supply, but the presence of hydrogen sulfide (H2S) poses significant safety risks and technical challenges to drilling operations. Taking the Tieshanpo high-sulfur gas reservoir as an example, its hydrogen sulfide content can reach as high as 222.30 g / m³. 3 During the drilling process, many problems are often encountered, such as well leakage, overflow, stuck drill bit and corrosion.

[0003] Currently, drilling parameter adjustments for high-sulfur formations rely heavily on experience, lacking a systematic and dynamic approach. This experience-based method leads to unreasonable parameter settings; for example, inappropriate drilling fluid density can cause lost circulation or overflows, while unsuitable drilling rates can exacerbate drill string wear and vibration. These problems not only increase the risk of H2S escape but also lead to safety accidents and significantly impact drilling efficiency. Therefore, there is an urgent need for a method that can dynamically optimize drilling parameters based on H2S escape risk to achieve safe and efficient drilling in high-sulfur formations, addressing the lack of scientific basis and dynamic adaptability in current parameter adjustments. Summary of the Invention

[0004] To address at least one of the aforementioned problems, this application provides a dynamic optimization method for drilling parameters in high-sulfur formations. This method considers the H2S escape risk index, constructs a dynamic optimization function that includes parameters such as drilling pressure and drilling speed, and solves the function to obtain the optimal drilling parameters.

[0005] To achieve the above objectives, this application provides a method for dynamic optimization of drilling parameters in high-sulfur formations, comprising the following steps:

[0006] Real-time acquisition of engineering parameters during the drilling process; the engineering parameters include drilling parameters to be optimized and hydrogen sulfide concentration;

[0007] Calculate the hydrogen sulfide escape risk index based on engineering parameters: In the formula, H2S risk C represents the hydrogen sulfide escape risk index. H2S ρ represents the hydrogen sulfide concentration, G represents the drilling fluid density, and k1, k2, and k3 all represent weights.

[0008] A dynamic optimization function is established and solved. The drilling parameter combination corresponding to the minimum value of the dynamic optimization function is the optimal drilling parameter combination. The dynamic optimization function is shown below:

[0009]

[0010] In the formula, ROP represents mechanical drilling rate, CORR represents corrosion rate, VIB represents vibration intensity, WOB represents drilling pressure, and E m To reduce the energy consumption of the drilling rig, D b Where is the drill bit diameter, RPM is the rotational speed, Q is the pump displacement, ρ is the drilling fluid density, T is the drilling rig torque, and α, β, and γ are weighting coefficients.

[0011] The beneficial effects of this invention are as follows: By real-time acquisition of key parameters such as H2S concentration and drilling fluid density, and combined with geological conditions, this invention calculates the H2S escape risk index, constructs a dynamic optimization function including parameters such as drilling pressure and drilling speed, and solves for the optimal drilling parameters. This method can significantly reduce the risk of hydrogen sulfide escape, increase mechanical drilling speed, reduce equipment corrosion and vibration, balance safety and efficiency, adapt to complex working conditions, and is applicable to various drilling scenarios in high-sulfur formations. Detailed Implementation

[0012] The technical solutions in the embodiments of this application will be clearly described below with reference to the embodiments of this application. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are all within the protection scope of this application.

[0013] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0014] A method for dynamic optimization of drilling parameters in high-sulfur formations includes:

[0015] Real-time acquisition of engineering parameters during the drilling process; the engineering parameters include drilling parameters to be optimized and hydrogen sulfide concentration;

[0016] Specifically, in this embodiment, engineering parameters are acquired by deploying distributed fiber optic sensors and wireless transmission modules inside the wellbore. These engineering parameters include drilling speed, drilling pressure, pump displacement, vibration intensity, hydrogen sulfide concentration, bottom hole temperature, and well inclination angle. The experimental parameters include drilling fluid density and corrosion rate.

[0017] In the specific implementation process, distributed fiber optic sensors and wireless transmission modules are arranged along the wellbore. The sensors include electrochemical detection sensors, which can monitor downhole hydrogen sulfide concentration, drilling fluid density, and vibration intensity in real time. Drilling rate, drilling pressure, pump displacement, and well inclination angle are conventional drilling parameters that can be obtained directly during the drilling process. Bottomhole temperature can be measured using a bottomhole thermometer. Corrosion rate can be experimentally determined by combining actual pressure, temperature, and hydrogen sulfide concentration; this is also standard practice in this field.

[0018] After the corresponding sensors and wireless transmission modules are installed, a corresponding receiving module can be set up in the ground control center to receive the measured parameter values.

[0019] In this embodiment, a well section (3500-3800m deep) of the high-sulfur gas reservoir in Tieshanpo is used as an example. The hydrogen sulfide content in this block is relatively high, with a peak value of 220g / m³. 3 The geological conditions are high-permeability sandstone reservoirs with a permeability of approximately 180–250 mD, a bottom-hole temperature of 70℃, and a well inclination angle of 25°.

[0020] Distributed fiber optic sensors and wireless transmission modules were deployed along the wellbore from 3500 to 3800 meters, with a monitoring interval of 5 meters. The final parameters obtained are as follows: hydrogen sulfide concentration 30–65 g / m³. 3 50g / m 3 The drilling fluid density is 1.2–1.3 g / cm³. 3 Drilling pressure 140-180 kN, rotation speed 70-90 rpm, pump displacement 28-32 L / s, corrosion rate 0.12-0.18 mm / year, vibration intensity 30-60 g.

[0021] Calculate the hydrogen sulfide escape risk index based on engineering parameters: In the formula, H2S risk C represents the hydrogen sulfide escape risk index. H2S ρ represents the hydrogen sulfide concentration, G represents the drilling fluid density, and k1, k2, and k3 all represent weights.

[0022] The geological risk G is used to characterize the ease or difficulty of drilling a formation. It is determined through pressure windows, drilling formulas, and drillability coefficients, and is expressed as:

[0023] G = 0.1H + η + (P) f -P p )

[0024] In the formula, H represents the well depth, in km; P p Represents the formation pressure equivalent density, in g / cm³ 3 P fThis represents the equivalent density of formation fracture pressure, expressed in g / cm³. 3 η represents the drilling difficulty of a formation, which is assigned a value based on the drillability of the formation. The drillability of a formation can be determined through indoor core testing. The drilling difficulty of a formation is assigned a value as shown in the table below.

[0025] Drillability 0-3 3-5 5-6 6-7 7-8 8-9 η 0.1 0.2 0.3 0.4 0.5 0.6

[0026] The weights k1, k2, and k3 are calculated using the entropy weight method based on historical drilling data collected from the sulfur-bearing strata in the work area. The steps are as follows:

[0027] Collect n drilling time data points for the sulfur-bearing strata in the work area. Each data point includes the hydrogen sulfide concentration, drilling fluid density, and geological risk at that moment, forming a matrix K, represented as:

[0028]

[0029] Furthermore, the columns are normalized and represented as follows:

[0030]

[0031] In the formula, z ji The result represents the normalization result, where i (i∈[1,n]) represents the row number and j (j∈[1,3]) represents the column number.

[0032] Then, the proportion ω of each parameter is calculated from the normalization results. ji , is represented as:

[0033]

[0034] Further determined by specific gravity ω ji Calculate the coefficient of variation d for each column (for each parameter) of matrix K. j , is represented as:

[0035]

[0036] Finally, through the coefficient of variation d j The values ​​of weights k1, k2, and k3 can then be calculated and expressed as:

[0037]

[0038] In this embodiment, the drillability of the sulfur-bearing strata in this work area is 6.73, and η = 0.4 is taken as P. p =1.32g / cm 3 P f =1.17g / cm 3 After calculation, k1 = 0.5, k2 = 0.3, k3 = 0.2, and G = 0.9. The final formula for calculating the hydrogen sulfide escape risk index is as follows: Substitute the measured average hydrogen sulfide concentration of 50 g / m³ into the equation. 3 Drilling fluid density 1.25 g / cm³ 3 Therefore, the escape risk index can be calculated to be 25.42.

[0039] A dynamic optimization function is established and solved. The drilling parameter combination corresponding to the minimum value of the dynamic optimization function is the optimal drilling parameter combination. The dynamic optimization function is shown below:

[0040]

[0041] In the formula, ROP represents mechanical drilling rate, CORR represents corrosion rate, VIB represents vibration intensity, WOB represents drilling pressure, and E m To reduce the energy consumption of the drilling rig, D b Where is the drill bit diameter, RPM is the rotational speed, Q is the pump displacement, ρ is the drilling fluid density, T is the drilling rig torque, and α, β, and γ are weighting coefficients.

[0042] In this step, since one of the key parameters of the dynamic optimization function is the weight coefficient, the following method is provided to calculate the weight coefficients α, β, and γ:

[0043] First, a scaling judgment matrix for the influencing factors is established using the analytic hierarchy process (AHP), as shown below:

[0044]

[0045] In the formula, p ij The scale value represents the i-th indicator relative to the j-th indicator, and the scale value is determined by on-site personnel based on the Santy table; n and m represent the order and number of columns of the matrix, which are determined by the number of selected indicators. In this embodiment, since the optimization parameters are drilling pressure, rotational speed, drilling fluid density, and displacement, corresponding scaling judgment matrices need to be established for these four parameters in actual operation.

[0046] Then, the test value of the scaling judgment matrix is ​​calculated:

[0047]

[0048] In the formula, CR represents the test value, λ is the rank of the scaling judgment matrix, n is the order of the scaling judgment matrix, and RI represents random consistency, which can be obtained by looking up a table. When the calculated test value is less than the first threshold, it indicates that the judgment matrix is ​​valid, and the next step can be performed; when the test value is not greater than the first threshold, it indicates that the judgment matrix is ​​invalid, and the scale values ​​in the scaling judgment matrix need to be reset. The first threshold can be set according to the actual situation; in this embodiment, it is set to 0.1.

[0049] Calculate the impact factor: In the formula, a i Indicates the impact factor; p ij represents the scale value of the i-th indicator relative to the j-th indicator, where i, j = 1, 2, ..., m; Π is the product operator.

[0050] The same operating method was used to calculate multiple influencing factors.

[0051] Finally, a scoring formula based on the weight coefficients of the influencing factors and the parameters to be optimized is established:

[0052] In the formula, S q Let q represent the rating values, where q = 1, 2, 3, and a i For the influence factor, i = 1, 2, ..., 9; T h This indicates the bottom temperature of the well.

[0053] The weighting coefficients α, β, and γ can be calculated using the above formula.

[0054] After obtaining the weighting coefficients α, β, and γ, the unknowns in the above dynamic optimization function are only the parameters to be optimized: drilling pressure, rotation speed, drilling fluid density, and displacement. Subsequently, in order to obtain the optimal parameters to be optimized, the dynamic optimization function needs to be solved.

[0055] In solving the problem, existing multi-objective nonlinear optimization methods can be used, such as the existing adaptive particle swarm optimization algorithm, the Eagle algorithm, and the Grey Wolf algorithm. All of these can be applied to this embodiment.

[0056] In this embodiment, the calculated weight coefficients α, β, and γ are 0.4, 0.2, and 0.1, respectively, the drill bit size is 311.5 mm, and the final expression of the optimization function is:

[0057]

[0058] Finally, an improved particle swarm optimization algorithm is used to solve the optimization function, and the optimal parameter combination is obtained as follows:

[0059] parameter Before optimization After optimization Adjustment range Drill pressure (WOB) 160kN 145kN -9.4％ Drilling speed (RPM) 85rpm 75rpm -11.8％ Drilling fluid density (ρ) <![CDATA[1.25g / cm 3 ]]> <![CDATA[1.28g / cm 3 ]]> +2.4％ Pump displacement (Q) 30L / s 29L / s -3.3％

[0060] After adjusting the drilling parameters according to the above procedures, the hydrogen sulfide concentration decreased to 42 g / m³. 3 The mechanical drilling speed increased from 7.2 m / h to 8.5 m / h, the maximum corrosion rate decreased from 0.16 mm / year to 0.13 mm / year, and the maximum vibration intensity decreased from 60 g to 40 g.

[0061] It should be noted that, upon considering the specification and practicing the application disclosed herein, those skilled in the art will readily conceive of other embodiments of this application. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

Claims

1. A method for dynamic optimization of drilling parameters in high-sulfur formations, characterized in that, include: Real-time acquisition of engineering parameters during the drilling process of the target well; the engineering parameters include drilling parameters to be optimized and hydrogen sulfide concentration; Calculate the hydrogen sulfide escape risk index based on engineering parameters: In the formula, H2S risk C represents the hydrogen sulfide escape risk index. H2S ρ represents the hydrogen sulfide concentration, G represents the drilling fluid density, and k1, k2, and k3 all represent weights. A multi-objective dynamic optimization function is established and solved. The drilling parameter combination corresponding to the minimum value of the dynamic optimization function is the optimal drilling parameter combination. The dynamic optimization function is shown below: In the formula, ROP represents mechanical drilling rate, CORR represents corrosion rate, VIB represents vibration intensity, WOB represents drilling pressure, and E m To reduce the energy consumption of the drilling rig, D b Where is the drill bit diameter, RPM is the rotational speed, Q is the pump displacement, ρ is the drilling fluid density, T is the drilling rig torque, and α, β, and γ are weighting coefficients.

2. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, By deploying distributed fiber optic sensors and wireless transmission modules inside the wellbore, and combining this with laboratory experiments, engineering parameters are obtained. These engineering parameters include drilling speed, drilling pressure, pump displacement, rotational speed, vibration intensity, hydrogen sulfide concentration, bottom hole temperature, drilling rig torque, drilling rig energy consumption, drill bit diameter, well inclination angle, drilling fluid density, and corrosion rate.

3. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, The weights are obtained using the entropy weighting method based on historical drilling data of the sulfur-bearing strata in the block where the target well is located.

4. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 3, characterized in that, The entropy weight method includes the following steps: Data on hydrogen sulfide concentration, drilling fluid density, and geological risk at multiple time points were acquired and normalized. Calculate the weight of each data point: In the formula, ω ji z represents the proportion of the i-th data in the j-th parameter. ji This represents the normalized value of the i-th data in the j-th parameter; Calculate the coefficient of variation for each parameter: In the formula, d j Represents the coefficient of variation of the j-th parameter; Calculate the weights:

5. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, The geological risk is calculated using the following formula: G = 0.1H + η + (P f -P p In the formula, H represents the well depth, and P... p P represents the equivalent density of formation pressure. f η represents the formation fracture pressure equivalent density; η represents the formation drilling difficulty.

6. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, The dynamic optimization function also includes the following constraints: In the formula, WOB MAX and RPM MAX These represent the maximum allowable drilling pressure and the maximum allowable drilling speed in the current work area, respectively; P p Represents the formation pressure equivalent density, in g / cm³ 3 P f This represents the equivalent density of formation fracture pressure, expressed in g / cm³. 3 .

7. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, The weighting coefficients α, β, γ, and δ are obtained through the following method: Establish the scoring formula for the weighted coefficients: In the formula, S q Representing the rating values, q = 1, 2, 3, a i For the influence factor, i = 1, 2, ..., 9; T h Indicates the bottom hole temperature; Based on the analytic hierarchy process (AHP), the impact factors are calculated, and the weight coefficients can be obtained by substituting the calculated impact factors into the above formula.

8. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 7, characterized in that, The method for calculating the impact factor includes the following steps: Establish a scaling judgment matrix for the influencing factors and calculate its consistency; Calculate the test value of the scaling judgment matrix. If the test value is less than the first threshold, it means that the scaling judgment matrix is ​​valid and proceed to the next step. If the test value is greater than the first threshold, redetermine the scaling judgment matrix of the influence factor. Calculate the impact factor: In the formula, a i Indicates the impact factor; p ij Let represent the scale value of the i-th indicator relative to the j-th indicator, where i, j = 1, 2, ..., m.

9. The method for dynamic optimization of drilling parameters in high-sulfur formations according to claim 1, characterized in that, Methods for solving dynamic optimization functions include adaptive particle swarm optimization, Eagle algorithm, and Grey Wolf algorithm.

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