A method for identifying oil content in low-saturation conglomerate reservoirs

Abstract

The present invention relates to the field of petroleum exploration, and specifically to a method for identifying the oil content of a low-saturated conglomerate reservoir, comprising the following steps: 1) analyzing the response characteristics of a conventional conglomerate curve to construct a lithology sensitivity curve; 2) constructing an oil-containing factor model that can reflect the oil content based on the lithology sensitivity curve obtained in step 1); 3) constructing a structural factor model that can reflect the reservoir pore structure; 4) establishing an intersection diagram of the oil-containing factor and the structural factor based on the two models of step 2) and step 3), determining the boundary between the oil and water layers, and dividing the fluid into an oil layer, an oil-water layer and a water layer based on its characteristics. The present invention, based on the analysis of rock physics, uses conventional logging data to construct two parameters, the oil-containing factor and the structural factor, and then uses existing oil test data to establish an intersection diagram, thereby realizing the identification of the oil and water layers of the low-saturated conglomerate reservoir.

Description

Technical Field

[0001] The invention relates to the field of petroleum exploration, and in particular to a method for distinguishing the oil content of a low-saturated sandstone conglomerate reservoir. Background Art

[0002] In oil and gas exploration, effective identification of oil and gas layers is an important part of well logging interpretation and oil testing layer selection. In particular, the improvement of well logging compliance can detect oil and gas layers early and save oil testing costs.

[0003] With the continuous deepening of oil and gas exploration, complex strata such as sandstone and conglomerate have gradually become the key areas of exploration. Compared with conventional sandstone and conglomerate, sandstone and conglomerate have complex lithology and large changes in parent rock composition. The logging response is seriously affected by the rock skeleton and pore structure. The logging response law is unclear, and the information reflecting the properties of reservoir pore fluid is weak, making it difficult to judge the properties of reservoir fluid. The heterogeneity is strong, and the development of fractures and dissolution pores significantly enhances the anisotropy and the pore structure is complex. The evaluation of sandstone and conglomerate reservoirs is a difficult problem in the field of petroleum exploration at home and abroad, and new theories and advanced technologies are urgently needed to support it.

[0004] Conventional oil and gas reservoirs, generally speaking, have an irreducible water saturation below 40%. In the normal process of oil field development, the irreducible water saturation basically exists inside the rock and will not change. For low-saturation oil and gas reservoirs, due to the complex state of fluid occurrence, the irreducible water content of low-saturation reservoirs will be relatively much higher, and during development, there will be very obvious movable irreducible water, which will lead to a corresponding decrease in oil and gas saturation, and a high flowback rate during the development of the oil field. The conventional logging response resolution of low-saturation reservoirs is poor, and the conventional logging interpretation and evaluation methods are not ideal.

[0005] The development trend of low saturation reservoir logging technology abroad is mainly to use conventional logging combined with nuclear magnetic resonance (Labani MM, Kadkhodaie-Ilkhchi A, Salahshoor K. Estimation of NMR log parameters from conventional well log data using a committee machine within intelligent systems: A case study from the Iranian part of the South Pars gasfield, Persian Gulf Basin [J]. Journal of Petroleum Science & Engineering, 2010, 72 (1): 175-185.), formation cable testing logging (Collett TS, Lewis RE, Winters WJ, et al. Downhole well log and coremontages from the Mount Elbert Gas Hydrate Stratigraphic Test Well, Alaska North Slope [J]. Marine & Petroleum Geology, 2011, 28(2): 561-577.) technology is used to comprehensively and qualitatively identify oil and water layers, and the oil saturation calculation is mostly evaluated using the WS model, double water model and shale sandstone model (Zhang Jie, Luo Jian, Xia Yu, et al. Applicability analysis and extension of Archie's formula [J]. Chinese Journal of Geophysics, 2018, 61(1): 311-322.; Zhang Min, Xu Bowen, Guan Linhu. Research on the calculation method of original oil saturation in Zhangjuhe Development Area of ​​Chenghai Oilfield [J]. Science and Technology Innovation and Application, 2018, No. 233(13): 44-45.; Hu Shengfu, Zhou Cancan, Li Xia, et al. Trapezoidal pore model for oil saturation of tight sandstone with complex pore structure [J]. Petroleum Exploration and Development, 2017, 44(5): 827-836.).

[0006] At present, researchers at home and abroad have done a lot of work on the study of sandstone and conglomerate reservoirs. However, due to the low resolution of conventional logging responses, little progress has been made in the identification of fluid properties in low-saturation sandstone and conglomerate reservoirs. Summary of the invention

[0007] The purpose of the present invention is to provide a method for identifying the oil content of low-saturation conglomerate reservoirs.

[0008] The purpose of the present invention is achieved through the following technical solutions.

[0009] A method for determining the oil content of a low-saturation conglomerate reservoir comprises the following steps:

[0010] 1) Analyze the response characteristics of the conventional curve of sandstone and construct the lithology sensitivity curve;

[0011] 2) Based on the lithology sensitivity curve obtained in step 1), an oil-bearing factor model capable of reflecting oil-bearing properties is constructed;

[0012] 3) Construct a structural factor model that can reflect the reservoir pore structure;

[0013] 4) Based on the two models of step 2) and step 3), establish the intersection diagram of oil content factor and structure factor, determine the boundary of oil and water layers, and divide the fluid into oil layer, oil-water layer and water layer according to its characteristics.

[0014] Furthermore, in step 1), the lithology sensitivity curve is determined by fitting the sonic time difference obtained by sonic logging and the neutron porosity obtained by neutron logging.

[0015] Furthermore, in step 1), the lithology sensitivity curve equation is as follows:

[0016] RCUR=Δt×(CNL-a)×b (1)

[0017] Among them, a and b are constants;

[0018] RCUR is the lithology sensitivity curve;

[0019] Δt is the acoustic time difference obtained by acoustic logging;

[0020] CNL is the neutron porosity obtained by neutron logging.

[0021] The inventors found that as the particle diameter decreases and the mud content in the reservoir increases, there is a correlation between the acoustic time difference, neutron porosity and rock properties, and a lithology sensitivity curve can be fitted. The lithology sensitivity curve thus fitted can eliminate the influence of different lithologies in the reservoir of the study area.

[0022] Furthermore, in step 2), the oil content factor model is corrected by the lithology sensitivity curve.

[0023] Furthermore, in step 2), the oil content factor model OF is as follows:

[0024]

[0025] Where a and b are constants;

[0026] OF is the oil content factor;

[0027] Δt is the acoustic time difference obtained by acoustic logging;

[0028] CNL is the neutron porosity obtained by neutron logging;

[0029] RT is the deep laterolog value;

[0030] ΔSP is the difference in the amplitude of the natural potential;

[0031] The inventors found that △SP, RT can reflect the oil content of a certain reservoir, but in areas where electrical properties are greatly affected by lithology, RT has an overlapping range. After correcting the results using the lithology sensitivity curve constructed in step 1), the influence of lithology can be eliminated and the oil content factor model OF that can comprehensively reflect the oil content can be achieved.

[0032] Furthermore, in step 3), the structure factor model is determined by fitting the pore volume and the particle volume.

[0033] Furthermore, in step 3), the structure factor model PSF is as follows:

[0034]

[0035] Where c is a constant;

[0036] PSF is the structure factor;

[0037] is the pore volume, unit: %;

[0038] Δt is the acoustic time difference obtained by acoustic logging;

[0039] Δt ma is the acoustic time difference of the rock skeleton;

[0040] Δt f is the acoustic time difference of pore fluid;

[0041] ρ b is the density logging value;

[0042] The inventors have found that the ratio of pore volume to particle volume can reflect the macroscopic pore structure characteristics. Combined with the actual geological conditions of the target area, a structural factor model that can reflect the reservoir pore structure can be constructed.

[0043] Furthermore, in step 4), the limit is determined by equation (4) and equation (5):

[0044] Bound 1: OF = dPSF + e (4)

[0045] Bound 2: OF = fPSF + g (5)

[0046] Among them, d, e, f, and g are constants obtained by fitting.

[0047] Furthermore, in step 4), the oil layer boundary is: OF≥dPSF+e and OF≥fPSF+g; the oil-water layer boundary is OF≤dPSF+e and OF≥fPSF+g; and the water layer boundary is OF≤fPSF+g.

[0048] The inventors found that the oil layer, oil-water layer and water layer models constructed by the method of the present invention improve the interpretation coincidence rate of oil and water layers in low-saturation conglomerate reservoirs.

[0049] The advantages of the present invention are:

[0050] 1. Based on the analysis of rock physics, the present invention uses conventional logging data to construct two parameters, oil content factor and structure factor, and then uses existing oil testing data to establish an intersection chart, thereby realizing the identification of oil and water layers in low-saturated conglomerate reservoirs.

[0051] 2. The present invention combines the advantages of rock physical analysis and mining conventional logging data, improves the coincidence rate of oil and water layer identification, and provides favorable technical guarantees for exploratory well oil testing layer selection, old well recovery oil testing, and reserve calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of a method for identifying oil content in a low-saturated conglomerate reservoir provided by the present invention;

[0053] Figure 2 It is a cross-plot of oil-water layer identification using oil-containing factor and structural factor according to an embodiment of the present invention;

[0054] Figure 3 This is an application effect diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Example 1

[0056] See also Figure 1 The present embodiment provides a method for determining the oil content of a low-saturated conglomerate reservoir, comprising the following steps:

[0057] Step 101: Analyze the response characteristics of the conventional curve of sandstone and construct a lithology sensitivity curve;

[0058] Step 102: constructing an oil content factor model capable of reflecting oil content using the sensitivity curve;

[0059] Step 103: constructing a structural factor model that can reflect the reservoir pore structure;

[0060] Step 104: Establish a cross plot of the oil content factor and the structure factor, determine the boundary of the oil-water layer, and divide the fluid into the oil layer, the oil-water layer, and the water layer according to its characteristics.

[0061] Example 2

[0062] According to the method of Example 1, the formula of the destination or model obtained in each step is determined.

[0063] in,

[0064] The lithology sensitivity curve in step 101 is as follows:

[0065] RCUR=Δt×(CNL-a)×b (1)

[0066] Among them, a and b are constants;

[0067] RCUR is the lithology sensitivity curve;

[0068] Δt is the acoustic time difference obtained by acoustic logging;

[0069] CNL is the neutron porosity obtained by neutron logging.

[0070] The oil content factor model OF in step 102 is as follows:

[0071]

[0072] Where a and b are constants;

[0073] OF is the oil content factor;

[0074] Δt is the acoustic time difference obtained by acoustic logging;

[0075] CNL is the neutron porosity obtained by neutron logging;

[0076] RT is the deep laterolog value;

[0077] ΔSP is the difference in the amplitude of the natural potential;

[0078] In step 103, the structure factor model PSF is as follows:

[0079]

[0080] Where c is a constant;

[0081] PSF is the structure factor;

[0082] is the pore volume, unit: %;

[0083] Δt is the acoustic time difference obtained by acoustic logging;

[0084] Δt ma is the acoustic time difference of the rock skeleton;

[0085] Δt fis the acoustic time difference of pore fluid;

[0086] ρ b is the density logging value;

[0087] The limit in step 104 is determined by equations (4) and (5):

[0088] Bound 1: OF = dPSF + e (4)

[0089] Bound 2: OF = fPSF + g (5)

[0090] Among them, d, e, f, and g are constants obtained by fitting.

[0091] The boundary of the oil layer is: OF≥dPSF+e and OF≥fPSF+g; the boundary between the oil and water layers is OF≤dPSF+e and OF≥fPSF+g; the boundary of the water layer is OF≤fPSF+g.

[0092] Example 3

[0093] This embodiment takes the sandstone and conglomerate formation sections of a specific oilfield block as the research target layer and constructs a method for distinguishing the oil content of the reservoir in the region.

[0094] 1. Analyze the response characteristics of the conventional curve of sandstone and construct the lithology sensitivity curve;

[0095] The geological data and logging data of the target layer are collected. The lithology of the target layer includes medium-fine sandstone, gravel-containing fine sandstone and conglomerate, etc. The pore types are mainly intergranular dissolved pores and primary intergranular pores, which are medium-pore and low-permeability reservoirs. The lithology of this study area is developed from coarse to fine, including conglomerate, gravel-containing fine sandstone, medium-fine sandstone, etc. It can be seen that the oil content of the reservoir in the study area is affected by different lithologies, among which the amount of mud content in different lithologies and the diameter of rock particles are its microscopic influencing factors. Rock physics analysis found that with the increase of clay content, neutrons become significantly larger. As the particle diameter decreases, the acoustic time difference gradually increases. Finally, the acoustic time difference of different lithologies and the neutron construction of lithology sensitivity curves are selected to eliminate the influence of different lithologies of the reservoir in the study area. In order to remove the influence of lithology, the corresponding multiples are magnified at the same time, and a=0.15 and b=10 are set.

[0096] RCUR=Δt×(CNL-0.15)×10 (1-1)

[0097] In the formula,

[0098] RCUR is the lithology sensitivity curve, dimensionless;

[0099] Δt is the acoustic logging value, unit is μs / m;

[0100] CNL is the neutron logging value, unit is f.

[0101] 2. By comparing the response characteristics of different logging curves in water layers and oil layers, the curve that is most sensitive to reservoir fluid identification is analyzed. It can be clearly seen that the △SP curve has a certain abnormal amplitude in the oil-water layer section. The electrical property (RT) can reflect the oil content of a certain reservoir, but the electrical property in this area is greatly affected by the lithology. The resistivity of each lithology has an overlapping range, so the electrical property curve alone cannot accurately distinguish the fluid properties. Therefore, a sensitive curve is used to construct an oil-containing factor model that can comprehensively reflect the oil content. The purpose is to amplify the response characteristics of the conventional curve to different fluids in the reservoir. In order to remove the influence of lithology, the corresponding multiples are amplified at the same time, and a=0.15 and b=10 are set.

[0102]

[0103] In the formula,

[0104] OF is the oil content factor, dimensionless;

[0105] Δt is the acoustic logging value, unit is μs / m;

[0106] CNL is the neutron logging value, unit is f;

[0107] RT is the deep laterolog value, unit: Ω·m;

[0108] ΔSP is the natural potential amplitude difference, unit is Mv;

[0109] 3. In the embodiment, the pore throat and micro pore throat are mainly present, and the pore throat structure preferably presents a single peak structure. According to the Kozeny-Carman equation, the ratio of pore volume to particle volume can reflect the characteristics of macroscopic pore structure. A structural factor model that can reflect the pore structure of the reservoir is constructed. Among them, the acoustic time difference value of the rock skeleton is 182μs / m, and the acoustic time difference value of the pore fluid is 620μs / m.

[0110]

[0111] In the formula,

[0112] PSF is the structure factor, dimensionless;

[0113] is the pore volume, unit: %;

[0114] Δt is the acoustic logging value, unit is μs / m;

[0115] Δt ma is the acoustic time difference of rock skeleton, unit is μs / m;

[0116] Δt f is the acoustic time difference of pore fluid, unit is μs / m;

[0117] ρ b Density logging value, unit: g / cm 3 ;

[0118] 4. Based on the data of the tested oil wells, establish the intersection diagram of oil content factor and structure factor to determine the distinction between oil and water layers (see Figure 2 ), and the fluid is divided into oil layer, oil-water layer, and water layer according to its characteristics. Using fitting, we get d = -63.33, e = 672.3, f = -25, g = 357.5.

[0119] Boundary 1: OF = -63.33PSF + 672.3 (4-1)

[0120] Boundary 2: OF = -25PSF + 357.5 (5-1)

[0121] Among them, the oil layer boundary is: OF≥-63.33PSF+672.3 and OF≥-25PSF+357.5; the oil-water layer boundary is OF≤-63.33PSF+672.3 and OF≥-25PSF+357.5; the water layer boundary is OF≤-25PSF+357.5.

[0122] 5. Figure 3 The effect diagram of the oil-bearing property identification method of the low-saturated conglomerate reservoir is shown in the figure. The first track is the natural gamma curve (GR), the natural potential amplitude difference curve (ΔSP), the second track is the depth track, the third track is the lithology profile, the fourth track is the deep lateral resistivity curve (RT), the fourth track is the three-porosity curve (Δt, DEN, CNL), the fifth track is the calculated oil-bearing factor track, the sixth track is the calculated structural factor track, and the seventh track is the oil test track. The OF calculated at the depth of 2578-2592m is 317.5, and the PST is 5.4. The data point (verification point) falls within the oil-water boundary ( Figure 2 ).

[0123] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A method for determining the oil content of a low-saturation conglomerate reservoir, comprising the following steps: 1) Analyze the response characteristics of the conventional curve of sandstone and construct the lithology sensitivity curve; 2) Based on the lithology sensitivity curve obtained in step 1), an oil-bearing factor model that can reflect oil-bearing properties is constructed; 3) Construct a structural factor model that can reflect the reservoir pore structure; 4) Based on the two models of step 2) and step 3), establish the intersection diagram of oil content factor and structure factor, determine the boundary of oil-water layer, and divide the fluid into oil layer, oil-water layer and water layer according to its characteristics; In step 1), the lithology sensitivity curve is determined by fitting the acoustic time difference obtained by acoustic logging and the neutron porosity obtained by neutron logging; In step 1), the lithology sensitivity curve equation is as follows: （1） Among them, a and b are constants; RCUR is the lithology sensitivity curve; The acoustic time difference obtained by acoustic logging; is the neutron porosity obtained by neutron logging; In step 2), the oil-bearing factor model is corrected by the lithology sensitivity curve; In step 2), the oil content factor model OF is as follows: （2） in is a constant; It is the oil-containing factor; The acoustic time difference obtained by acoustic logging; is the neutron porosity obtained by neutron logging; is the deep laterolog value; is the natural potential amplitude difference; In step 3), the structure factor model is determined by fitting the pore volume and the particle volume; In step 3), the structure factor model PSF is as follows: in, is a constant; is the structure factor; is the pore volume, unit: % The acoustic time difference obtained by acoustic logging; is the acoustic time difference of the rock skeleton; is the acoustic time difference of pore fluid; is the density logging value.

2. The method according to claim 1, characterized in that In step 4), the limit is determined by equations (4) and (5) Boundary 1: (4) Boundary 2: (5) Among them, d, e, f, and g are constants obtained by fitting.

3. The method according to claim 2, characterized in that In step 4), the oil layer boundary is: and ; The boundary between oil and water layers is and The water layer boundary is .

Images