CN101963056B - Method for predicting carbonate formation pore pressure by using log information - Google Patents

Abstract

The invention discloses a method for predicting carbonate formation pore pressure by using log information. The method for predicting the carbonate formation pore pressure by using the log information is based on the effective stress theorem; and by establishing a framework longitudinal wave velocity and pore fluid longitudinal wave velocity equation, a carbonate formation pore pressure equation is established, so that the carbonate formation pore pressure is detected according to the measured log information, scientific evidences are provided for determining safety drilling fluid density during drilling design, and carbonate formation underground complex accidents in the construction process are effectively prevented.

Description

A kind of method of utilizing well-log information Predicting Carbonate Formation pore pressure

Technical field

The present invention relates to a kind of method of utilizing well-log information Predicting Carbonate Formation pore pressure, more particularly, relate to a kind ofly based on effective stress theorem, consider skeleton velocity of longitudinal wave and pore-fluid velocity of longitudinal wave, utilize well-log information to carry out the method for Predicting Carbonate Formation pore pressure.

Background technology

In hill carbonate stratum, have a large amount of undeveloped oil gas, this is the new exploratory area of the following stable yields in oil field.Carbonate rock distribution is extensive, and the oil fields such as huge port, Tarim Basin, Jiang-Han Area, Sichuan, triumph, Central Plains, North China, Xinjiang, Qinghai, long celebrating are all at exploitation carbonate rock hydrocarbon reservoir.The interstitial space complex structure that carbonate formation has, as crack and hole, and the factors such as its anisotropy, non-homogeneity have been aggravated the complexity of carbonate sediment process, cause can not accurately determining the formation mechanism of carbonate formation abnormal pore high pressure, thereby be difficult to find rational interpretation model.Bore to meet carbonate formation, due to abnormal pressure prediction difficulty, often can cause cave-in, leakage, even the down-hole complex accident such as blowout.If can Accurate Prediction For Pore Pressure on Carbonate Rock Formation, just can be and determine that safe drilling fluid density window provides scientific basis, effectively to stop the generation of carbonate formation down-hole complex accident.

Creator in the present invention relies on it to be engaged in for many years experience and the practice of relevant industries for this reason, and through concentrating on studies and developing, finally creates a kind of method that utilization utilizes well-log information Predicting Carbonate Formation pore pressure.

Summary of the invention

The object of the present invention is to provide a kind of method of utilizing well-log information Predicting Carbonate Formation pore pressure; utilize the method can Predicting Carbonate Formation pore pressure; so that when Drilling Design for determining that safe drilling fluid density provides scientific basis; protection carbonate rock hydrocarbon reservoir, effectively to stop the generation of carbonate rock down-hole complex accident.

The method of utilizing well-log information Predicting Carbonate Formation pore pressure in the present invention, includes the following step:

1) in drilling process, utilize sieve residue log to determine carbonate rock interval, get core and test the effective stress p of rock sample _e, coefficient of cubical compressibility C _ma, confined pressure p and skeleton velocity of longitudinal wave v _ma, obtain the rock mechanics parameters characteristic series { C of rock sample _ma ⁽ⁱ⁾, p ⁽ⁱ⁾, { p _e ⁽ⁱ⁾, v _ma ⁽ⁱ⁾;

2) in During Oil Testing Process, record composition x, saturation state y, temperature T, pressure p, the density p of oil sample, measure the velocity of longitudinal wave v of oil sample _f, obtain the characteristic series { x of oil sample ⁽ⁱ⁾, y ⁽ⁱ⁾, T ⁽ⁱ⁾, p ⁽ⁱ⁾, ρ ⁽ⁱ⁾, v _f ⁽ⁱ⁾;

3), according to the characteristic series of rock sample, by multiple nonlinear regression method, set up skeleton velocity of longitudinal wave equation and be: v _ma=f (C _ma, P _e);

4), according to the characteristic series of oil sample, by multiple nonlinear regression method, set up pore-fluid velocity of longitudinal wave equation and be: v _f=f (ρ, p, T);

5) by empirical formula, the velocity of longitudinal wave equation of setting up carbonate formation is: v=f (v _f, v _ma, φ);

6), according to effective stress theorem, by the velocity of longitudinal wave equation of skeleton velocity of longitudinal wave and pore-fluid velocity of longitudinal wave equation substitution carbonate formation, set up formation pore pressure predictive equation, p _p=f (ρ _f, T, C _ma, v, p ₀, φ);

7) pore pressure detects:

(A) stratum is carbonate rock,

(B) set up skeleton velocity of longitudinal wave equation,

(C) set up pore-fluid velocity of longitudinal wave equation,

(D) set up formation pore pressure predictive equation,

(E) by the temperature of the velocity of longitudinal wave of different depth place carbonate formation, coefficient of cubical compressibility, overburden pressure, oil sample, pressure, density, degree of porosity substitution p _p=f (ρ _f, T, C _ma, v, p ₀, φ), obtain the formation pore pressure at different depth place.

Method in described step 1) is to carry out rock mechanics parameters experiment, sets up effective stress p _e, coefficient of cubical compressibility C _ma, confined pressure p and skeleton velocity of longitudinal wave v _madS { C _ma ⁽ⁱ⁾, p ⁽ⁱ⁾, { p _e ⁽ⁱ⁾, v _ma ⁽ⁱ⁾.

Described step 2) method in is on-the-spot record oil sample data, and laboratory experiment in addition, sets up composition x, saturation state y, temperature T, pressure p, density p, the velocity of longitudinal wave v of oil sample _fdS { x ⁽ⁱ⁾, y ⁽ⁱ⁾, T ⁽ⁱ⁾, p ⁽ⁱ⁾, ρ ⁽ⁱ⁾, v _f ⁽ⁱ⁾.

Method in described step 3) is by ordered sequence { C _ma ⁽ⁱ⁾, p ⁽ⁱ⁾, { p _e ⁽ⁱ⁾, v _ma ⁽ⁱ⁾carry out Multiple Non Linear Regression, and obtain the relation between coefficient of cubical compressibility and confined pressure, effective stress and skeleton velocity of longitudinal wave, set up skeleton velocity of longitudinal wave equation and be: v _ma=f (C _ma, P _e).

Method in described step 4) is by ordered sequence { v _f ⁽ⁱ⁾, x ⁽ⁱ⁾, y ⁽ⁱ⁾, T ⁽ⁱ⁾, p ⁽ⁱ⁾, ρ ⁽ⁱ⁾carry out Multiple Non Linear Regression, and obtain the velocity of longitudinal wave of oil sample and the relation between temperature, pressure and density, set up pore-fluid velocity of longitudinal wave equation and be: v _f=f (ρ, p, T).

Method in described step 5) is to utilize the characteristic of carbonate formation, considers degree of porosity, sets up the velocity of longitudinal wave of carbonate formation and the equation v=f (v between skeleton velocity of longitudinal wave and pore-fluid velocity of longitudinal wave _f, v _ma, φ).

Method in described step 6) is according to effective stress theorem, sets up formation pore pressure predictive equation, p _p=f (ρ _f, T, C _ma, v, p ₀, φ).

Method in described step 7) is first to confirm carbonate rock feature, by non-linear regression method, sets up skeleton velocity of longitudinal wave equation v _ma=f (C _ma, P _e) and pore-fluid velocity of longitudinal wave equation v _f=f (ρ, p, T), according to effective stress theorem, sets up formation pore pressure predictive equation p _p=f (ρ _f, T, C _ma, v, p ₀, φ), by the temperature of the velocity of longitudinal wave of different depth place carbonate formation, coefficient of cubical compressibility, overburden pressure, oil sample, pressure, density, degree of porosity substitution formation pore pressure predictive equation, obtain the formation pore pressure at different depth place.

The method of utilizing well-log information Predicting Carbonate Formation pore pressure in the present invention is based on effective stress theorem, by setting up skeleton velocity of longitudinal wave and pore-fluid velocity of longitudinal wave equation, with this, set up For Pore Pressure on Carbonate Rock Formation equation, thereby detect For Pore Pressure on Carbonate Rock Formation according to the log data recording, so that when Drilling Design for determining that safe drilling fluid density provides scientific basis, effectively to stop the generation of carbonate formation down-hole complex accident in work progress.

Accompanying drawing explanation

Fig. 1 is carbonate formation log response characteristic pattern;

Fig. 2 is the For Pore Pressure on Carbonate Rock Formation result figure that utilizes well-log information to detect.

The specific embodiment

Below in conjunction with accompanying drawing, the instantiation in the present invention is described in further detail.

According to effective stress theorem, overburden pressure is by frame stress and pore fluid pressure shared, and in normal discharging consolidation process, rock porosity increases and diminishes with total stress (overburden pressure).The sedimentation mechanism of carbonate formation is different from Clastic Stratum of Country Rocks.According to THE THEORY OF ELASTIC WAVE, sound wave is propagated in stratum, will inevitably pass through rock matrix and pore-fluid, the velocity of sound (velocity of longitudinal wave) can reflect that rock matrix and pore-fluid form situation, and think that the velocity of sound (velocity of longitudinal wave) is dimerous by matrix velocity and pore-fluid speed, the velocity of sound (velocity of longitudinal wave) is relevant with factors such as effective stress, bulk modulus, pore-fluid density, formation pore pressure, pore-fluid temperature.Therefore utilize the well-log information response characteristic can Predicting Carbonate Formation pore pressure situation.

In the present invention, utilize the method for well-log information Predicting Carbonate Formation pore pressure to comprise the following steps:

1. determine carbonate rock interval

By sieve residue log data, determine carbonate rock interval, and core at carbonate formation; In laboratory, carry out rock sample and core, be processed into standard rock core, carry out rock mechanics parameters test experiments, obtain the rock mechanics characteristic series { C of rock sample _ma ⁽ⁱ⁾, p ⁽ⁱ⁾, { p _e ⁽ⁱ⁾, v _ma ⁽ⁱ⁾, as follows:

$\left\{\begin{array}{cc}{C}_{\mathrm{ma}}& P_e\\ 0& 0.000184\\ 10& 0.000452\\ 15& 0.000393\\ 20& 0.000376\\ 30& 0.00034\\ 40& 0.000304\\ 50& 0.000299\\ 60& 0.000285\\ 70& 0.000271\\ 80& 0.00025\end{array}\right\}$ $\left\{\begin{array}{cc}{P}_e& v_{\mathrm{ma}}\\ 10& 5.62\\ 20& 5.68\\ 30& 5.74\\ 40& 5.79\\ 50& 5.84\\ 60& 5.86\\ 70& 5.88\\ 80& 5.91\end{array}\right\}$

2. in During Oil Testing Process, record composition x, saturation state y, temperature T, pressure p, the density p of oil sample, measure the velocity of longitudinal wave v of oil sample _f, and carry out laboratory experiment, obtain the characteristic of oil sample.

3. by the rock mechanics characteristic to rock sample, carry out Multiple Non Linear Regression, obtaining coefficient of cubical compressibility and confined pressure is exponential relationship, and confined pressure can represent by effective stress, thinks coefficient of cubical compressibility and effective stress exponent function relation; Effective stress and skeleton velocity of longitudinal wave are linear.The skeleton velocity of longitudinal wave equation that can set up thus rock sample is $v_{\mathrm{ma}}=a_1+a_2{e}^{C_{\mathrm{ma}}}+a_3{p}_e.$

4. pass through ordered sequence { v _f ⁽ⁱ⁾, x ⁽ⁱ⁾, y ⁽ⁱ⁾, T ⁽ⁱ⁾, p ⁽ⁱ⁾, ρ ⁽ⁱ⁾carry out Multiple Non Linear Regression, obtain the velocity of longitudinal wave of oil sample and density, temperature and pressure is linear respectively, linear with the product of temperature and pressure.Setting up pore-fluid velocity of longitudinal wave equation is υ _f=b ₁ρ _f-b ₂t+b ₃p _p+ b ₄tp _p.

5. formula rule of thumb, the velocity of longitudinal wave equation of setting up carbonate formation is

$\frac{1}{v_p}={\left(\frac{1}{v_f}\right)}^{\mathrm{\φ}}\·{\left(\frac{1}{v_{\mathrm{ma}}}\right)}^{(1-\mathrm{\φ})}.$

6. based on effective stress theorem, have:

P _on=P _p+ P _e

By the velocity of longitudinal wave equation of skeleton velocity of longitudinal wave and pore-fluid velocity of longitudinal wave equation substitution carbonate formation, set up formation pore pressure predictive equation, have:

$\frac{1}{v_p}={\left(\frac{1}{{\mathrm{<figure-callout\; id="1"\; label="b"\; filenames="HSA00000235290900011.png,HSA00000235290900021.png"\; state="\{\{state\}\}">b</figure-callout>}}_1{\mathrm{\&rho;}}_f-{\mathrm{<figure-callout\; id="2"\; label="b"\; filenames="HSA00000235290900011.png"\; state="\{\{state\}\}">b</figure-callout>}}_2\&CenterDot;T+b_3\&CenterDot;p_p+b_4\&CenterDot;T\&CenterDot;p_p}\right)}^{\mathrm{\&phi;}}\&CenterDot;{\left(\frac{1}{a_1+a_2{e}^{C_{\mathrm{ma}}}+a_3{p}_e}\right)}^{(1-\mathrm{\&phi;})}.$

7. utilize acoustic logging data, the formation pore pressure data according to actual measurement, return out model parameter in formation pore pressure predictive equation, that is:

a ₁=0.14387,a ₂=2.51628,a ₃=0.15674

b ₁=0.25146,b ₂=0.04715,b ₃=0.02843,b ₄=0.10913

Can utilize thus this model prediction For Pore Pressure on Carbonate Rock Formation.

Claims (6)

1. a method of utilizing well-log information Predicting Carbonate Formation pore pressure, includes the following step:

1), in drilling process, utilize sieve residue log data to determine carbonate rock interval, the effective stress of getting core and testing rock sample

, coefficient of cubical compressibility , confined pressure p _m with skeleton velocity of longitudinal wave , the rock mechanics parameters characteristic that obtains rock sample is serial ,

;

2), in During Oil Testing Process, record the composition of oil sample x, saturation state y, temperature t, pressure p, density

, the velocity of longitudinal wave of measurement oil sample

, the characteristic that obtains oil sample is serial

;

3), according to the characteristic series of rock sample, by multiple nonlinear regression method, set up skeleton velocity of longitudinal wave equation and be:

;

4), according to the characteristic series of oil sample, by multiple nonlinear regression method, set up oil sample velocity of longitudinal wave equation and be:

;

5) by empirical formula, the velocity of longitudinal wave equation of setting up carbonate formation is:

;

6) according to effective stress theorem, by the velocity of longitudinal wave equation of skeleton velocity of longitudinal wave and oil sample velocity of longitudinal wave equation substitution carbonate formation, set up formation pore pressure predictive equation, ; Described

refer to respectively overburden pressure and overlying rock degree of porosity;

7) pore pressure detects:

(A) stratum is carbonate rock,

(B) set up skeleton velocity of longitudinal wave equation,

(C) set up oil sample velocity of longitudinal wave equation,

(D) set up formation pore pressure predictive equation,

(E) by the temperature of the velocity of longitudinal wave of different depth place carbonate formation, coefficient of cubical compressibility, overburden pressure, oil sample, density, degree of porosity substitution

, obtain the formation pore pressure at different depth place.

2. according to the method for utilizing well-log information Predicting Carbonate Formation pore pressure described in claim 1, it is characterized in that: the method in described step 1) is to carry out rock mechanics parameters test experiments, sets up effective stress

, coefficient of cubical compressibility

, confined pressure p _m with skeleton velocity of longitudinal wave

dS

,

.

3. according to the method for utilizing well-log information Predicting Carbonate Formation pore pressure described in claim 1, it is characterized in that: the method described step 2) is on-the-spot record oil sample data, and laboratory experiment in addition, set up composition x, saturation state y, temperature T, pressure p, the density of oil sample

, velocity of longitudinal wave

dS .

4. according to the method for utilizing well-log information Predicting Carbonate Formation pore pressure described in claim 1, it is characterized in that: the method in described step 3) is by ordered sequence

,

carry out Multiple Non Linear Regression, obtain the relation between coefficient of cubical compressibility and confined pressure, effective stress and skeleton velocity of longitudinal wave, set up skeleton velocity of longitudinal wave equation and be: .

5. according to the method for utilizing well-log information Predicting Carbonate Formation pore pressure described in claim 1, it is characterized in that: the method in described step 4) is by ordered sequence carry out Multiple Non Linear Regression, obtain the relation between the velocity of longitudinal wave of oil sample and temperature, density, pressure, set up oil sample velocity of longitudinal wave equation and be:

.

6. according to the method for utilizing well-log information Predicting Carbonate Formation pore pressure described in claim 1, it is characterized in that: the method in described step 5) is to utilize the characteristic of carbonate formation, consider degree of porosity, set up the velocity of longitudinal wave of carbonate formation and the equation between skeleton velocity of longitudinal wave and oil sample velocity of longitudinal wave

.

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