Fractured oil and gas reservoir evaluation method based on logging curve

Abstract

The invention belongs to the technical field of oil exploration and development, and particularly relates to a fractured oil and gas reservoir evaluation method based on a logging curve. In order to solve the problem that in the prior art, a fractured oil and gas reservoir needs to be evaluated through well drilling coring and electric imaging, so that the evaluation cost is high, a horizontal principal stress variation coefficient of a logged well is obtained through calculation according to well logging data and is fitted with electric imaging fracture data, and an evaluation threshold value of the horizontal principal stress variation coefficient is obtained; according to the method, a rapid and effective technical means is provided for exploration and development of oil and gas reservoirs, the accuracy rate is high, the research period is short, the cost is low, operation is easy, and the method is suitable for popularization and application. The requirement for identifying cracks and evaluating fractured oil and gas reservoirs in the oil and gas resource exploration and development stage is met, cost is greatly saved, and exploration and development benefits are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil exploration and development, and particularly relates to a method for evaluating fractured hydrocarbon reservoirs based on well logging curves. Background Art

[0002] Fractured hydrocarbon reservoirs globally account for nearly half of the total proven reserves. Currently, the vast majority of the world's highest-producing wells of 10,000 tons are related to fractured hydrocarbon reservoirs in carbonate rocks. For example, in many oil fields of this type such as the Gachsaran Oilfield in Iran, the daily production of individual wells mostly reaches over 1,000 tons, and some wells of 10,000 tons have been stable for over a decade. The reason for such high productivity is closely related to the size of the reservoir space and its storage performance. The existence of fractures greatly improves the storage performance of the reservoir body, and fractures provide channels for hydrocarbon migration.

[0003] The study of fractures has a history of at least over 100 years. Among them, the core observation method and the outcrop observation method in the field are the basis for fracture logging and geological research. The core observation method is the most direct means to study fractures in a single well, but it has limitations. It is impossible to take cores for every well due to the limited cores. The microscopic observation method is for microfractures, and its results include thin sections and electron microscope scans, provided that drilling cores are available. Due to the high cost of core sampling and electrical imaging data, usually only a few exploration wells have core sampling and electrical imaging data, resulting in an incomplete understanding of the fracture development and an inability to effectively guide the exploration and development deployment of hydrocarbon reservoirs. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for evaluating fractured hydrocarbon reservoirs based on well logging curves to solve the problem in the prior art that the evaluation of fractured hydrocarbon reservoirs requires drilling core sampling and electrical imaging, resulting in high evaluation costs.

[0005] To solve the above technical problems, the present invention provides a method for evaluating fractured hydrocarbon reservoirs based on well logging curves, including the following steps:

[0006] 1) Calculate the coefficient of variation of the horizontal principal stress of the well with measured well logging data in the target oil reservoir.

[0007] 2) Analyze various data included in the electrical imaging fracture data of the well with measured well logging data in the target oil reservoir respectively with the coefficient of variation of the horizontal principal stress, select a certain type of electrical imaging fracture data with the highest correlation with the coefficient of variation of the horizontal principal stress, and use the value of the coefficient of variation of the horizontal principal stress corresponding to the boundary between fracture development and non-development as the threshold for judging whether fractures are developed, and then establish a classification evaluation standard for hydrocarbon enrichment degree in combination with the factor parameters affecting hydrocarbon enrichment degree.

[0008] 3) Evaluate the hydrocarbon enrichment degree of the well to be measured by using the coefficient of variation of the horizontal principal stress of the well to be measured, the above-mentioned factor parameters, and in combination with the established classification evaluation standard.

[0009] Furthermore, various types of data of the electrical imaging fracture data include fracture length, fracture width, and hydrodynamic width.

[0010] Furthermore, the methane content is used as a factor parameter affecting the hydrocarbon enrichment degree.

[0011] Furthermore, in the classification evaluation criteria, it is divided into four categories: Class I, Class II, Class III, and Class IV according to the hydrocarbon enrichment degree, and it satisfies Class I hydrocarbon enrichment degree > Class II hydrocarbon enrichment degree > Class III hydrocarbon enrichment degree > Class IV hydrocarbon enrichment degree; among them, Class I is that fractures are developed and the methane content is greater than b% and less than 100%, Class II is that fractures are developed and the methane content is greater than a% and less than or equal to b%, or fractures are underdeveloped and the methane content is greater than b% and less than 100%, Class III is that fractures are developed and the methane content is less than or equal to a%, or fractures are underdeveloped and the methane content is greater than or equal to a% and less than or equal to b%, Class IV is that fractures are underdeveloped and the methane content is less than a%, where a% < b < 100%.

[0012] Furthermore, the highest correlation with the coefficient of variation of the horizontal principal stress is the fracture length.

[0013] Furthermore, the calculation formula for the coefficient of variation of the horizontal principal stress is:

[0014]

[0015] Among them, C.V is the coefficient of variation of the horizontal principal stress; σ h is the minimum horizontal principal stress; σ H is the maximum horizontal principal stress.

[0016] Its beneficial effects are as follows: To solve the problem that the prior art needs to evaluate fractured hydrocarbon reservoirs through drilling coring and electrical imaging, resulting in high evaluation costs, the present invention calculates the coefficient of variation of the horizontal principal stress of the logged wells obtained from logging data, and fits it with the electrical imaging fracture data to obtain the evaluation threshold of the coefficient of variation of the horizontal principal stress, and combines the factor parameters affecting the hydrocarbon enrichment degree as the classification evaluation criteria for the hydrocarbon enrichment degree to evaluate the wells to be logged. The present invention provides a fast and effective technical means for the exploration and development of hydrocarbon reservoirs, with high accuracy, short research cycle, low cost, and simple operation, meeting the needs of identifying fractures and evaluating fractured hydrocarbon reservoirs in the exploration and development stages of hydrocarbon resources, greatly saving costs, and improving exploration and development benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a flowchart of the method of the embodiment of the present invention;

[0018] Figure 2 is an EMI imaging logging result diagram of the embodiment of the present invention;

[0019] Figure 3 It is a comparison chart of the EMI imaging logging result map of Well L1 and the coefficient of variation of the horizontal principal stress in the embodiment of the present invention;

[0020] Figure 4 It is a comparison chart of the EMI imaging logging result map of Well L1-1 and the coefficient of variation of the horizontal principal stress in the embodiment of the present invention;

[0021] Figure 5 It is an intersection chart of the fracture length of Well L1-1 and the coefficient of variation of the horizontal principal stress in the embodiment of the present invention;

[0022] Figure 6 It is an intersection chart of the apparent width of fractures in Well L1-1 and the coefficient of variation of the horizontal principal stress in the embodiment of the present invention;

[0023] Figure 7 It is an intersection chart of the hydrodynamic width of Well L1-1 and the coefficient of variation of the horizontal principal stress in the embodiment of the present invention;

[0024] Figure 8 It is an intersection chart of the coefficient of variation of the horizontal principal stress and GR in the embodiment of the present invention;

[0025] Figure 9 It is the production curve of Well K in the embodiment of the present invention. Detailed implementation manners

[0026] The basic concept of the present invention is as follows: The present invention calculates the coefficient of variation of the horizontal principal stress based on the logging data of the well-logged target reservoir, establishes the corresponding relationship between the coefficient of variation of the horizontal principal stress and various data of the electro-imaging fractures of the well-logged wells to obtain the classification and evaluation criteria for fractured reservoirs, and evaluates the hydrocarbon enrichment situation according to the established classification criteria. The specific principle of the present invention is: Calculate the coefficient of variation of the horizontal principal stress according to the well-logging data of the well-logged target reservoir obtained, establish the corresponding relationship between the coefficient of variation of the horizontal principal stress of this well and various data of the electro-imaging fractures according to the electro-imaging data of the well-logged target reservoir obtained, and obtain the evaluation threshold of the coefficient of variation of the horizontal principal stress according to the data with the highest correlation, and establish the classification and evaluation criteria for hydrocarbon enrichment degree in combination with other factor parameters affecting hydrocarbon enrichment degree to evaluate the enrichment degree of the well to be logged. Based on this concept, a method for evaluating fractured hydrocarbon reservoirs based on logging curves of the present invention can be realized.

[0027] The present invention will be described in detail below in conjunction with the accompanying drawings and method embodiments.

[0028] Method embodiment:

[0029] Based on the oilfield where Block L is located, there is currently 388 km² of 3D seismic exploration area 2 , with a total of 35 exploration wells drilled, and the total footage is 90,790 meters. Block L has a predicted reserve of 608.51×10 4At time t, 5 wells were drilled to completion. The reservoir depth is 3,200 - 3,340 m. The reservoir is composed of calcareous conglomerate, and the reservoir type is fractured reservoir. A method for evaluating fractured oil and gas reservoirs based on logging curves according to the present invention has a flow chart as shown in Figure 1 and the specific implementation steps are as follows:

[0030] Step 1: Collect the conventional logging data and electrical imaging data of Block L. As shown in Figure 2 , the fractured wells and their intervals in 2 wells with electrical imaging data in Block L were counted. In Well L1-1, there were 44 fractures in the interval of 3,103.27 - 3,420.84 m, with fracture lengths ranging from 0.71 to 1.632 m and average apparent widths ranging from 11.42 to 62.82 mm. In Well L1, there were 7 fractures in the interval of 3,145 - 3,345.5 m, with fracture lengths ranging from 1.01 to 1.85 m.

[0031] Step 2: Use the SAOR reservoir in-situ stress software to calculate the in-situ stress parameters of 5 wells including Well L1, Well L1-1, and LP1 using logging data: minimum horizontal principal stress (SDYM) and maximum horizontal principal stress (SDXM).

[0032] Step 3: Calculate the coefficient of variation of the horizontal principal stress for each well from the in-situ stress parameters obtained in Step 2 through Formula (1). The specific formula is as follows:

[0033]

[0034] where C.V is the coefficient of variation of the horizontal principal stress; σ h is the minimum horizontal principal stress; σ H is the maximum horizontal principal stress.

[0035] Taking Well L1-1 as an example, the coefficient of variation of the horizontal principal stress of this well is shown in Table 1 below:

[0036] Table 1

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048] Step 4: Establish the corresponding relationship between the electrical imaging fracture data and the coefficient of variation of horizontal principal stress for Well L1-1 and Well L1, as shown in Figure 3 and Figure 4 Taking Well L1-1 as an example, the cross-plot of fracture length and the coefficient of variation of horizontal principal stress is as shown in Figure 5 ; the cross-plot of fracture width and the coefficient of variation of horizontal principal stress is as shown in Figure 6 ; the cross-plot of hydrodynamic width and the coefficient of variation of horizontal principal stress is as shown in Figure 7 ; select the electrical imaging fracture data with a relatively high correlation with the coefficient of variation of horizontal principal stress. There is a positive correlation between fracture length and the coefficient of variation of horizontal principal stress, that is, the greater the coefficient of variation of horizontal principal stress, the longer the fracture length. Therefore, judge whether the fracture is developed according to the fracture length, and use the coefficient of variation of horizontal principal stress corresponding to the boundary between developed and undeveloped fractures as the evaluation threshold.

[0049] In the well sections of 3295 - 3298m, 3323 - 3326m, 3329.5 - 3330.5m, and 3338.2 - 3339m of Well L1-1, the electrical imaging data interpretation shows that fractures (tensile fractures) are developed, and the coefficient of variation of horizontal principal stress is extremely high and greater than 2.3 in the nearly same well sections. The coefficient of variation of horizontal principal stress of 2.3 is used as the boundary between developed and undeveloped fractures in this block.

[0050] Step 5: Establish the classification and evaluation criteria for fractured reservoirs. Classify and evaluate the fractured reservoirs in Block L using the coefficient of variation of horizontal principal stress and the factor parameters affecting hydrocarbon enrichment. The factor parameter selected in this embodiment is methane content. The specific classification criteria are shown in Table 2 below:

[0051] Table 2

[0052]

[0053]

[0054] When the methane content 1% < C1 < 100% and the coefficient of variation of horizontal principal stress ≥ 2.3, it is a Class I reservoir with developed fractures and rich hydrocarbon accumulation.

[0055] When the methane content 1% < C1 < 100% and the coefficient of variation of horizontal principal stress < 2.3, or 0.1% ≤ C1 ≤ 1% and the coefficient of variation of horizontal principal stress ≥ 2.3, it is a Class II reservoir.

[0056] When the methane content is 0.1% ≤ C1 ≤ 1% and the coefficient of variation of the horizontal principal stress is < 2.3, or when C1 < 0.1% and the coefficient of variation of the horizontal principal stress is ≥ 2.3, it is a Class III reservoir.

[0057] When the methane content C1 < 0.1% and the coefficient of variation of the horizontal principal stress is < 2.3, it is a Class IV reservoir, where fractures are not well-developed and oil and gas are not enriched.

[0058] Step Six: Classify and evaluate the oil and gas reservoirs. According to the classification criteria obtained in Step Five, judge the well sections of the single-well fracture development horizons. For example, in Well LP1, the cross-plot of the coefficient of variation of the horizontal principal stress and GR for the well sections of 3204 - 3209m, 3320 - 3325m, 3343 - 3349m, and 3389 - 3401m is as Figure 8 shown. The coefficient of variation of the horizontal principal stress is 0.06 - 16.2, and its methane content is 0.018% - 20.969%. According to the standards, there is 1 Class I, 7 Class II, and 8 Class III, as shown in Table 3 below.

[0059] Table 3

[0060]

[0061]

[0062] As Figure 9 shown, based on this evaluation criterion, the perforation target layer of Well K6 is 20.8m / layer, with a daily oil production of 6 tons and stable production for 14 months, thus verifying the applicability and reliability of the present invention.

[0063] Based on logging curves, the present invention calculates the coefficient of variation of the horizontal principal stress, establishes the corresponding relationship between the coefficient of variation of the horizontal principal stress and the electrical imaging fractures, and combines the methane content to establish a classification and evaluation criterion for fractured reservoirs, so as to realize the identification of reservoir fractures and the evaluation of the enrichment of fractured oil and gas. The present invention greatly reduces the cost of reservoir fracture evaluation and meets the needs of identifying fractures and evaluating fractured oil and gas reservoirs during the exploration and development stages of oil and gas resources.

Claims

1. A method for evaluating fractured hydrocarbon reservoirs based on well logging curves, characterized in that, it includes the following steps: 1) Calculate the coefficient of variation of the horizontal principal stress of the well-logged target reservoir using the well logging data of the well-logged target reservoir; 2) Analyze each type of data included in the electrical imaging fracture data of the well-logged target reservoir separately with the coefficient of variation of the horizontal principal stress, select a certain type of electrical imaging fracture data with the highest correlation with the coefficient of variation of the horizontal principal stress, and use the value of the coefficient of variation of the horizontal principal stress corresponding to the boundary between fracture development and non-development as the threshold for judging whether fractures are developed, and then establish a classification and evaluation standard for hydrocarbon enrichment degree in combination with the factor parameters affecting hydrocarbon enrichment degree; 3) Evaluate the hydrocarbon enrichment degree of the well to be logged using the coefficient of variation of the horizontal principal stress of the well to be logged, the said factor parameters, and in combination with the established classification and evaluation standard.

2. The method for evaluating fractured hydrocarbon reservoirs based on well logging curves according to claim 1, characterized in that, each type of data of the electrical imaging fracture data includes fracture length, fracture width, and hydrodynamic width.

3. The method for evaluating fractured hydrocarbon reservoirs based on well logging curves according to claim 1, characterized in that, methane content is used as the factor parameter affecting hydrocarbon enrichment degree.

4. The method for evaluating fractured hydrocarbon reservoirs based on well logging curves according to claim 3, characterized in that, in the classification and evaluation standard, it is divided into four categories: Class I, Class II, Class III, and Class IV according to hydrocarbon enrichment degree, and it satisfies Class I hydrocarbon enrichment degree > Class II hydrocarbon enrichment degree > Class III hydrocarbon enrichment degree > Class IV hydrocarbon enrichment degree; where Class I is fracture developed and methane content is greater than b% and less than 100%, Class II is fracture developed and methane content is greater than a% and less than or equal to b%, or fracture underdeveloped and methane content is greater than b% and less than 100%, Class III is fracture developed and methane content is less than or equal to a%, or fracture underdeveloped and methane content is greater than or equal to a% and less than or equal to b%, Class IV is fracture underdeveloped and methane content is less than a%, a% < b < 100%.

5. The method for evaluating fractured hydrocarbon reservoirs based on well logging curves according to claim 2, characterized in that, the one with the highest correlation with the coefficient of variation of the horizontal principal stress is the fracture length.

6. The method for evaluating fractured hydrocarbon reservoirs based on well logging curves according to claim 1, characterized in that, the calculation formula for the coefficient of variation of the horizontal principal stress is: Among them, C.V is the coefficient of variation of the horizontal principal stress; σ h is the minimum horizontal principal stress; σ H is the maximum horizontal principal stress.

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