Logging lithology identification method suitable for carbonate reservoir

Abstract

The invention belongs to the technical field of oil-gas exploration and development, and discloses a logging lithology identification method suitable for carbonate reservoirs, and the method comprises the following steps: 1) collecting logging information of a target well, including a natural gamma curve and a natural potential curve; 2) according to logging information, calculating a lithology index V of a target depth by adopting a specific formula; and 3) according to the lithology index V, determining the reservoir lithology of the target depth. According to the method, the lithology of the carbonate reservoir is divided into five categories according to the complex lithology distribution condition of the carbonate reservoir, the lithology index well logging interpretation model is established, the discrimination standard of each kind of lithology is determined, the lithology identification method suitable for the carbonate reservoir is formed, and the lithology can be rapidly and accurately identified.

Description

Technical Field

[0001] The present invention belongs to the technical field of oil and gas exploration and development, and in particular relates to a well logging lithology identification method applicable to carbonate reservoirs. Background Art

[0002] Carbonate reservoirs play a crucial role in the global distribution of oil and gas. Compared to sandstone reservoirs, they possess a greater complexity and diversity, with diverse reservoir space types and significant secondary variability. Previous logging evaluation methods for carbonate reservoirs relied on the traditional sandstone-shale logging model, creating logging intersection charts based on the delineated reservoir lithology to identify oil-water dry zones. With increasing exploration and development, the oil-water relationship in these reservoirs has become increasingly problematic, with instances of low-resistance oil production and high-resistance water production, as well as significant resistivity differences between different sandstone formations. Therefore, logging evaluation of these reservoirs urgently needs to be improved.

[0003] As major domestic oilfields delve deeper into complex lithologies and oil-water relationships, there's a growing awareness that accurate lithology identification is crucial for reservoir evaluation. Carbonate reservoirs contain not only sandstone and mudstone, but also other complex lithologies, which is why traditional sandstone and mudstone logging models are no longer applicable. Therefore, accurate lithology identification is a prerequisite for comprehensive logging evaluation of carbonate reservoirs.

[0004] Currently, the most effective method for lithologic identification is core analysis. However, considering production efficiency, the high cost of coring deep wells makes it impossible to perform coring in every well. Therefore, indirect methods for lithologic identification are inevitable. However, there is still a lack of an accurate indirect lithologic identification method for carbonate reservoirs. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to address the deficiencies in the existing technology and provide a logging lithology identification method suitable for carbonate reservoirs. The lithology of carbonate reservoirs is divided into five categories. By establishing a lithology index logging interpretation model, the discrimination criteria for each type of lithology are established, forming a lithology identification method suitable for carbonate reservoirs, which can quickly and accurately identify lithology.

[0006] To solve the technical problem raised by the present invention, the present invention provides a well logging lithology identification method applicable to carbonate reservoirs, comprising the following steps:

[0007] 1) Collect logging data of the target well, including natural gamma ray curve and natural potential curve;

[0008] 2) Based on the well logging data, the lithology index V at the target depth is calculated using the following formula:

[0009]

[0010] Where, SP max is the maximum value of the spontaneous potential curve, SP min is the minimum value of the natural potential curve, SP is the natural potential logging value at the target depth, GR max is the maximum value of the natural gamma curve, GR min is the minimum value of the natural gamma ray curve, GR is the natural gamma ray logging value at the target depth;

[0011] 3) Determine the reservoir lithology at the target depth based on the lithology index V:

[0012] When V < 0.5, the reservoir lithology is limestone or mudstone;

[0013] When 0.5≤V<1, the reservoir lithology is mudstone, granular limestone or granular dolomite;

[0014] When 1≤V≤1.5, the reservoir lithology is sandstone, granular limestone or granular dolomite;

[0015] When V>1.5, the reservoir lithology is sandstone.

[0016] In the above scheme, the logging data also includes one or more of the acoustic time difference logging value AC, the photoelectric absorption cross-section index logging value PE, the micro-gradient logging value RMN, and the micro-potential logging value RML, which are used to further identify the reservoir lithology when the lithology index V cannot determine the reservoir lithology.

[0017] In the above scheme, when the lithology index V cannot determine the reservoir lithology, the reservoir lithology can be further identified in combination with the acoustic time difference logging value AC, where the unit of AC is μs / m:

[0018] When AC≤300, the reservoir lithology is limestone, grain limestone or grain dolomite;

[0019] When 300<AC≤350, the reservoir lithology is limestone, sandstone or granular dolomite;

[0020] When 350<AC<400, the reservoir lithology is sandstone, mudstone or granular dolomite;

[0021] When AC≥400, the reservoir lithology is mudstone or sandstone.

[0022] Furthermore, when AC≤300 and 0.5≤V≤0.75, the reservoir lithology is granular dolomite; when AC≤300 and 0.75<V≤1.5, the reservoir lithology is granular limestone.

[0023] In the above scheme, when the lithology index V cannot determine the reservoir lithology, the micro-gradient logging value RMN and the micro-potential logging value RML can be further combined to identify the reservoir lithology. The units of RMN and RML are Ω·m:

[0024] When RML≤3 and |RML-RMN|≤0.05, the reservoir lithology is mudstone;

[0025] When RML≤3 and 0.05<|RML-RMN|≤2, the reservoir lithology is sandstone;

[0026] When RML>3 and |RML-RMN|≤1, the reservoir lithology is limestone, grain limestone or grain dolomite.

[0027] In the above scheme, when the lithology index V cannot determine the reservoir lithology, the reservoir lithology can be further identified in combination with the photoelectric absorption cross-section index logging value PE. The unit of PE is B / e:

[0028] When PE < 3, the reservoir lithology is sandstone;

[0029] When 3≤PE≤3.5, the reservoir lithology is mudstone;

[0030] When PE>3.5, the reservoir lithology is limestone, granular dolomite or granular limestone.

[0031] In the above scheme, the limestone includes limestone and limy dolomite, which has no effective pores and is a non-reservoir layer; the granular dolomite is a reservoir layer with a dolomite content greater than 50% and developed pores; the granular limestone is a reservoir layer with developed intergranular pores, good connectivity and a surface porosity of more than 20%.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This method, based on extensive basic research, addresses the complex lithologic distribution of carbonate reservoirs and categorizes carbonate reservoir lithologies into five broad categories. Based on this, a lithologic index logging interpretation model, combined with other logging parameters, establishes criteria for distinguishing each type of lithology, resulting in a well logging lithologic identification method suitable for carbonate reservoirs. This method is simple to operate, highly accurate, and universally applicable, with broad prospects for widespread adoption. Furthermore, it plays a crucial role in the well logging evaluation of complex carbonate reservoirs, laying a solid foundation for the subsequent classification and establishment of interpretation criteria for oil-water dry layers. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is the lithology identification map of Examples 1-3 of the present invention.

[0035] Figure 2 This is the lithology identification map of Example 4-5 of the present invention. DETAILED DESCRIPTION

[0036] In order to better understand the present invention, the content of the present invention is further illustrated below in conjunction with the examples, but the content of the present invention is not limited to the following examples.

[0037] Example 1

[0038] Taking Well A in the BM area as an example, the reservoir lithology at the target depth of 1829.5m is identified:

[0039] 1) Collect the logging data of well a and obtain:

[0040] Maximum value of the spontaneous potential curve SP max =35mV, minimum value of the spontaneous potential curve SP min =15mV, the natural potential logging value SP at the target depth = 26mV;

[0041] The maximum value of the natural gamma curve GR max =77API, minimum value of natural gamma curve GR min =34API, the natural gamma ray logging value GR of the target depth = 60API;

[0042] The acoustic transit time logging value at the target depth is AC = 305 μs / m, the micro-gradient logging value is RMN = 2.2 Ω·m, and the micro-potential logging value is RML = 3.0 Ω·m;

[0043] 2) Calculate the lithologic index V at the target depth based on the well logging data:

[0044]

[0045] 3) Based on 1≤V≤1.5, the reservoir lithology at the target depth is judged to be sandstone, granular limestone or granular dolomite;

[0046] Furthermore, combined with 300<AC≤350, the reservoir lithology at the target depth is judged to be sandstone or granular dolomite;

[0047] Furthermore, combined with RML≤3 and 0.05<|RML-RMN|≤2, the reservoir lithology at the target depth is determined to be sandstone.

[0048] To verify the accuracy of the lithology identification results, core sampling was performed in Well A, and core analysis revealed that the reservoir lithology at the target depth was indeed sandstone, with a silt content of 95% and a very fine sand content of 5%, primarily composed of quartz and feldspar. This demonstrates the accuracy of the method presented herein.

[0049] Example 2

[0050] Taking Well A in the BM area as an example, the reservoir lithology at the target depth of 1915.6m is identified:

[0051] 1) Collect the logging data of well a and obtain:

[0052] Maximum value of the spontaneous potential curve SP max =35mV, minimum value of the spontaneous potential curve SP min =15mV, the natural potential logging value SP at the target depth = 29mV;

[0053] The maximum value of the natural gamma curve GR max =77API, minimum value of natural gamma curve GR min =34API, the natural gamma ray logging value GR of the target depth = 53API;

[0054] The acoustic transit time logging value at the target depth is AC = 325 μs / m;

[0055] 2) Calculate the lithologic index V at the target depth based on the well logging data:

[0056]

[0057] 3) Based on 0.5≤V<1, the reservoir lithology at the target depth is judged to be mudstone, granular limestone or granular dolomite;

[0058] Furthermore, combined with 300<AC≤350, it is determined that the reservoir lithology at the target depth is granular dolomite.

[0059] To verify the accuracy of the lithology identification results, core sampling was performed in Well A, and core analysis revealed that the reservoir lithology at the target depth was indeed granular dolomite, containing 59.5% dolomite, 12% calcite, 0.5% pyrite, 13% quartz, 8% feldspar, 2% clay, and 5% rock debris. The method of the present invention provides accurate results.

[0060] Example 3

[0061] Taking well a in the BM area as an example, the reservoir lithology at the target depth of 1925m is identified:

[0062] 1) Collect the logging data of well a and obtain:

[0063] Maximum value of the spontaneous potential curve SP max =35mV, minimum value of the spontaneous potential curve SP min =15mV, the natural potential logging value SP at the target depth = 35mV;

[0064] The maximum value of the natural gamma curve GR max =77API, minimum value of natural gamma curve GR min =34API, the natural gamma ray logging value GR of the target depth = 77API;

[0065] The acoustic transit time logging value at the target depth is AC = 352 μs / m, the micro-gradient logging value is RMN = 0.7 Ω·m, and the micro-potential logging value is RML = 0.7 Ω·m;

[0066] 2) Calculate the lithologic index V at the target depth based on the well logging data:

[0067]

[0068] At this time, the lithologic index V cannot be calculated and is directly identified by combining it with other logging parameters;

[0069] 3) Further combining 350<AC<400, the reservoir lithology at the target depth is judged to be sandstone, mudstone or granular dolomite;

[0070] Furthermore, combined with RML≤3 and |RML-RMN|≤0.05, the reservoir lithology at the target depth is determined to be mudstone.

[0071] In order to verify the accuracy of the lithology identification results, core sampling was carried out in Well a and core analysis was carried out. The results showed that the reservoir lithology at the target depth was indeed mudstone, with a clay content of 52.5% and a limestone content of 2.6%, indicating that the results of the method of the present invention are accurate.

[0072] Example 4

[0073] Taking well b in the BM area as an example, the reservoir lithology at the target depth of 1827.8m is identified:

[0074] 1) Collect the logging data of well b and obtain:

[0075] Maximum value of the spontaneous potential curve SP max =53mV, the minimum value of the spontaneous potential curve SP min =12mV, the natural potential logging value SP at the target depth = 52mV;

[0076] The maximum value of the natural gamma curve GR max =102API, minimum value of natural gamma curve GR min =30API, the natural gamma ray logging value GR of the target depth = 42API;

[0077] The acoustic transit time logging value at the target depth is AC = 238 μs / m;

[0078] 2) Calculate the lithologic index V at the target depth based on the well logging data:

[0079]

[0080] 3) Based on V < 0.5, the reservoir lithology at the target depth is judged to be limestone or mudstone;

[0081] Further combined with AC≤300, it is determined that the reservoir lithology at the target depth is limestone.

[0082] In order to verify the accuracy of the lithology identification results, core sampling was carried out in well b and core analysis was carried out. The results showed that the reservoir lithology at the target depth was indeed limestone (confirmed by rock logging: bubbling of hydrochloric acid dripping), indicating that the results of the method of the present invention are accurate.

[0083] Example 5

[0084] Taking well b in the BM area as an example, the reservoir lithology at the target depth of 1868.6m is identified:

[0085] 1) Collect the logging data of well b and obtain:

[0086] Maximum value of the spontaneous potential curve SP max =53mV, the minimum value of the spontaneous potential curve SP min =12mV, the natural potential logging value SP at the target depth is =12mV;

[0087] The maximum value of the natural gamma curve GR max =102API, minimum value of natural gamma curve GR min =30API, the natural gamma ray logging value GR of the target depth is 31API;

[0088] The acoustic transit time logging value at the target depth is AC = 291 μs / m, the micro-gradient logging value is RMN = 3.2 Ω·m, and the micro-potential logging value is RML = 3.8 Ω·m.

[0089] 2) Calculate the lithologic index V at the target depth based on the well logging data:

[0090]

[0091] 3) Based on 1≤V≤1.5, the reservoir lithology at the target depth is judged to be sandstone, granular limestone or granular dolomite;

[0092] Furthermore, combined with AC≤300 and 0.75<V≤1.5, the reservoir lithology at the target depth is determined to be granular limestone.

[0093] To verify the accuracy of the lithology identification results, core sampling was performed in Well B and core analysis was conducted. The results showed that the reservoir lithology at the target depth was indeed granular limestone. Thin sections showed well-developed intergranular pores, good connectivity, and a porosity of 20%, demonstrating that the method of the present invention is accurate.

[0094] The above embodiments are merely examples for clarification and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications may be made based on the above descriptions. It is not necessary and impossible to enumerate all implementation methods here, and any obvious variations or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. A well logging lithology identification method applicable to carbonate reservoirs, characterized in that: The following steps are involved: 1) Collect logging data of the target well, including natural gamma ray curve and natural potential curve; 2) Based on the well logging data, the lithology index V at the target depth is calculated using the following formula: Where, SP max is the maximum value of the spontaneous potential curve, SP min is the minimum value of the natural potential curve, SP is the natural potential logging value at the target depth, GR max is the maximum value of the natural gamma curve, GR min is the minimum value of the natural gamma ray curve, GR is the natural gamma ray logging value at the target depth; 3) Determine the reservoir lithology at the target depth based on the lithology index V: When V < 0.5, the reservoir lithology is limestone or mudstone; When 0.5≤V<1, the reservoir lithology is mudstone, granular limestone or granular dolomite; When 1≤V≤1.5, the reservoir lithology is sandstone, granular limestone or granular dolomite; When V>1.5, the reservoir lithology is sandstone.

2. The well logging lithology identification method applicable to carbonate reservoirs according to claim 1, characterized in that: The logging data also includes one or more of the acoustic time difference logging value AC, the photoelectric absorption cross section index logging value PE, the micro gradient logging value RMN, and the micro potential logging value RML.

3. The well logging lithology identification method applicable to carbonate reservoirs according to claim 1, characterized in that: When the lithology index V cannot determine the reservoir lithology, the reservoir lithology is further identified by combining the acoustic time difference logging value AC. The unit of AC is μs / m: When AC≤300, the reservoir lithology is limestone, grain limestone or grain dolomite; When 300<AC≤350, the reservoir lithology is limestone, sandstone or granular dolomite; When 350<AC<400, the reservoir lithology is sandstone, mudstone or granular dolomite; When AC≥400, the reservoir lithology is mudstone or sandstone.

4. The well logging lithology identification method applicable to carbonate reservoirs according to claim 3, characterized in that: When AC≤300 and 0.5≤V≤0.75, the reservoir lithology is granular dolomite; when AC≤300 and 0.75<V≤1.5, the reservoir lithology is granular limestone.

5. The well logging lithology identification method applicable to carbonate reservoirs according to claim 1, characterized in that: When the lithology index V cannot determine the reservoir lithology, the micro-gradient logging value RMN and the micro-potential logging value RML are further combined to identify the reservoir lithology. The units of RMN and RML are Ω·m: When RML≤3 and |RML-RMN|≤0.05, the reservoir lithology is mudstone; When RML≤3 and 0.05<|RML-RMN|≤2, the reservoir lithology is sandstone; When RML>3 and |RML-RMN|≤1, the reservoir lithology is limestone, grain limestone or grain dolomite.

6. The well logging lithology identification method applicable to carbonate reservoirs according to claim 1, characterized in that: When the lithology index V is unable to determine the reservoir lithology, the photoelectric absorption cross-section index logging value PE is further combined to identify the reservoir lithology. The unit of PE is B / e: When PE < 3, the reservoir lithology is sandstone; When 3≤PE≤3.5, the reservoir lithology is mudstone; When PE>3.5, the reservoir lithology is limestone, granular dolomite or granular limestone.

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