CN104156617B - For the six stage modeling methods that multilayer sandstone reservoirs gas-bearing formation attribute classification is characterized - Google Patents

Abstract

The invention discloses a modeling method for the classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs, which relates to a six-stage modeling method. The present invention focuses on the multi-layered structure formed by the stable plane deposition and vertical sand-mud interbeds of the thousand-layer pie-shaped multi-layered sandstone gas reservoir, and proposes two-level structure modeling, two-level sub-facies modeling and four-type reservoir attribute modeling The new method establishes a six-stage modeling method system of stratum structure modeling-sand body structure modeling-sand body microfacies modeling-reservoir facies modeling-reservoir attribute modeling-gas layer quality classification modeling, realizing Accurate and quantitative characterization of gas layer quality in three-dimensional space in multi-layer sandstone gas reservoirs with thousand layers of pie. The multi-layer sandstone gas reservoir gas quality classification model obtained based on the present invention is more accurate and finer than the gas reservoir model built by the traditional three-stage modeling method, and is widely applicable to gas reservoirs in the middle and later stages of multi-layer sandstone gas reservoir development The urgent need for classified balanced development.

Description

Six-stage modeling method for classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs

technical field

The invention relates to a six-stage modeling method, in particular to a six-stage modeling method for the classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs.

Background technique

At present, there is no modeling method specifically aimed at the classification and characterization of multi-layer sandstone gas reservoir gas layer quality at home and abroad. When establishing a multi-layer sandstone gas reservoir model, people mainly use the traditional structural modeling-sedimentary facies modeling-reservoir A Three-Stage Modeling Approach for Layer Attribute Modeling.

The shortcomings of the traditional three-stage modeling method of structure-sedimentation-reservoir attributes for the classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs are reflected in the following three aspects: (1) Multi-layer sandstone gas reservoirs are characterized by sand-mud interbeds Multi-layer structure, sand body structure is one of the main factors controlling the fluid distribution of gas reservoirs, but traditional methods only focus on the modeling of stratigraphic structural planes, without considering sand body structure modeling; (2) Multi-layer sandstone gas reservoirs are generally located in coastal and shallow lake (sea) environment, the plane microfacies of sand bodies are stable, but affected by diagenesis, it is difficult to correspond one-to-one between sand body microfacies and effective reservoirs, and the traditional method only establishes sand body microfacies models without considering the establishment of reservoirs (3) The traditional modeling method only considers the controlling effect of structure and sand body micro-relative to reservoir properties, and it is difficult to realize the controlling effect of sand body structure and reservoir relative to reservoir properties. It can be seen that the traditional modeling method is an extensive modeling method, which is mainly used in the early stage of multi-layer sandstone gas reservoir development where the requirements for the accuracy of gas reservoir description are generally not high. Obviously, this method is difficult to adapt to the increasing accuracy of gas reservoir description in the middle and late stages of multi-layer sandstone gas reservoir development.

Multi-layered sandstone gas reservoirs belonging to the shore-shallow lake (marine) subfacies are widely developed in Qinghai Oilfield, Changqing Oilfield, Sichuan Gasfield, Xinjiang Oilfield, Daqing Oilfield, Shengli Oilfield, etc., and most of these gas reservoirs have entered the In the middle and late stage of development, the pressure of sustained and stable production of gas reservoirs is increasing, and it is urgent to adopt new technologies to realize the classification and characterization of gas reservoir quality. However, it is difficult to meet the actual needs in terms of the precision of the modeling results. Therefore, it is urgent to develop a new modeling method that fully considers the characteristics of multi-layer sandstone gas reservoirs and is accurate and precise.

Contents of the invention

Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a six-stage modeling method for the classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs, through stratum structure modeling-sand body structure modeling-sand body The six-stage modeling method of microfacies modeling-reservoir facies modeling-reservoir property modeling-gas layer quality classification modeling aims to realize the quality of gas layers in three-dimensional space Accurate and quantitative characterization provides technical and method support for the fine description of multi-layer sandstone gas reservoirs.

In order to achieve the above object, the present invention is realized through a technical scheme of a six-stage modeling method for the classification and characterization of gas layer quality in multi-layer sandstone gas reservoirs. The method steps include:

(A) Two-level structure modeling: The basic principles of two-level structure modeling are shown in formulas (1) and (2). The first level is stratum structure modeling. Using stratum division and comparison, the top and bottom altitude data pairs W _i of the stratum top and bottom at m well points are used to establish and form the stratum top and bottom structures through the Kriging deterministic modeling algorithm f Model S; the second level is sand body structure modeling, relying on the constraints of the top and bottom S of the stratum, the altitude data W _ij of the top and bottom of the sand body at the m well points obtained by sand body division and comparison, Through the Kriging deterministic modeling algorithm f, the top and bottom structural models S _j of n sand bodies in the formation are established to realize the distribution prediction of the formation and sand bodies in three-dimensional space.

Level 1: Stratigraphic Modeling

The second level: sand body structure modeling

In the formula: F——is mapping.

(B) Two-level secondary facies modeling: The basic principles of two-level secondary facies modeling are shown in formulas (3) and (4). The first stage is sand body microfacies modeling, which is directly established by the Kriging deterministic modeling algorithm f by using the drawn sand body microfacies plane distribution map. The second stage is reservoir facies modeling, the input data is single well reservoir distribution data obtained from single well reservoir identification, and the reservoir facies model is established by using stochastic simulation algorithms such as sequential indicator simulation or indicator Kriging; During the process, the reservoir facies model is always placed under the constraints of the sand body microfacies model, so that the reservoir facies between well points can only walk randomly in the airspace defined by the sand body microfacies.

Level 1: Sand body microfacies modeling

Level 2: Reservoir Facies Modeling

In the formula: F——mapping; GSF——a data set based on the digitalization of sand body microfacies distribution map drawn by geologists; SF——established sand body microfacies model; WRE—single well reservoir facies Data set; RE—reservoir facies model established; m—number of wells.

(C) Modeling of four types of attributes: the three attribute models of porosity, gas saturation and permeability are established using the reservoir facies control method, the basic principle is shown in formula (5); the gas layer quality classification attribute is directly passed through the above attribute model Generate, the basic principle is shown in formula (6).

Reservoir facies-controlled three-attribute modeling

Modeling of gas layer quality classification attributes

In the formula: F—mapping; PROP _ij —single well property data; fg—sequential Gaussian stochastic simulation algorithm; RE—reservoir facies model; MPROP _j —property model; fff—gas layer Classification attribute parameter standard; GCLASS—gas reservoir quality classification attribute model; j=1 for porosity, 2 for gas saturation, 3 for permeability; m—number of wells.

Beneficial effects of the present invention: the multi-layer sandstone gas reservoir gas layer quality classification model obtained based on the present invention is more accurate and finer than the gas reservoir model built by the traditional three-stage modeling method, and is widely applicable to multi-layer sandstone gas reservoirs There is an urgent need for classified and balanced development of gas reservoirs in the middle and late stages of development.

Description of drawings

The present invention is described in detail below in conjunction with accompanying drawing and specific embodiment;

Fig. 1 is technical method flowchart of the present invention;

Fig. 2 is a three-dimensional characterization of a certain multi-layer sandstone gas field formation-sand layer-interlayer combination structure in the present invention (upper left is the section line position figure, and upper right is the stratum structure diagram in three-dimensional space; the main body is the well formation-sand layer- Sandwich composite structure cross-section);

Fig. 3 is a three-dimensional characterization of sand body microfacies and reservoir facies in a certain multi-layer sandstone gas field of the present invention. ((1), (2), and (3) are the sand body microfacies of sublayer 0-2-2, sublayer 0-2-5 and sublayer 0-2-4, respectively, (4), (5), (6) Reservoir facies of 0-2-2 sublayer, 0-2-3 sublayer and 0-2-4 sublayer respectively.)

Fig. 4 is the three-dimensional characterization diagram of the four types of attribute models of the 0-2-2 sub-layer gas layer quality of a certain multi-layer sandstone gas field of the present invention; ((1) porosity model, (2) permeability model, (3) gas-bearing Saturation model, (4) Gas layer quality classification model.)

Fig. 5 is a three-dimensional characterization of four types of attribute models of the quality of 0-2-3 sub-layer gas layers in a certain multi-layer sandstone gas field of the present invention ((1) porosity model, (2) permeability model, (3) gas saturation model, (4) gas layer quality classification model);

Fig. 6 is a three-dimensional characterization of the four-type attribute model of the gas layer quality of 0-2-4 sub-layers in a certain multi-layer sandstone gas field of the present invention. ((1) porosity model, (2) permeability model, (3) gas saturation model, (4) gas layer quality classification model.)

detailed description

In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

Referring to Fig. 1, the present embodiment adopts the following technical solutions: the present invention revolves around the multi-layered structure formed by plane sedimentation stability and vertical sand-mud interbeds of the thousand-layer pie-shaped multi-layer sandstone gas reservoir, and proposes two-level structural modeling, two-level New ideas for subfacies modeling and four types of reservoir attribute modeling, established stratum structure modeling-sand body structure modeling-sand body microfacies modeling-reservoir facies modeling-reservoir attribute modeling-gas layer The six-stage modeling method system of quality classification modeling (Fig. 1) has realized the accurate and quantitative characterization of gas layer quality in three-dimensional space in the layered pie-shaped multi-layered sandstone gas reservoir.

The basic principles of the two-level structural modeling are shown in formulas (1) and (2). The first level is stratum structure modeling. Using stratum division and comparison, the top and bottom altitude data pairs W _i of the stratum top and bottom at m well points are used to establish and form the stratum top and bottom structures through the Kriging deterministic modeling algorithm f Model S; the second level is sand body structure modeling, relying on the constraints of the top and bottom S of the stratum, the altitude data W _ij of the top and bottom of the sand body at the m well points obtained by sand body division and comparison, Through the Kriging deterministic modeling algorithm f, the top and bottom structural models S _j of n sand bodies in the formation are established to realize the distribution prediction of the formation and sand bodies in three-dimensional space.

Level 1: Stratigraphic Modeling

The second level: sand body structure modeling

In the formula: F——is mapping.

Figure 2 shows the 0-2-2 (divided into two sand bodies A and B) and 0-2-3 (divided into A, B, C three sand bodies) of a certain multi-layer sandstone gas field completed by the two-level structural modeling method. sand body) and 0-2-4 (divided into three sand bodies A, B, and C) and the stratigraphic structure and sand body structure modeling results of three sublayers.

The basic principles of the two-level secondary phase modeling are shown in formulas (3) and (4). The first level is sand body microfacies modeling, which is directly established by the kriging deterministic modeling algorithm f by using the drawn sand body microfacies plane distribution map. The second stage is reservoir facies modeling, the input data is single well reservoir distribution data obtained from single well reservoir identification, and the reservoir facies model is established by using stochastic simulation algorithms such as sequential indicator simulation or indicator Kriging; During the process, the reservoir facies model is always placed under the constraints of the sand body microfacies model, so that the reservoir facies between well points can only walk randomly in the airspace defined by the sand body microfacies.

Level 1: Sand body microfacies modeling

Level 2: Reservoir Facies Modeling

In the formula: F——mapping; GSF——a data set based on the digitalization of sand body microfacies distribution map drawn by geologists (0 is mud flat, 1 is sand flat, and 2 is sand bar); SF—establishment Formed sand body microfacies model; WRE—single well reservoir facies data set (0 means non-reservoir, 1 means reservoir); RE—established formed reservoir facies model; m—number of wells.

Fig. 3 shows the sand body microfacies models and corresponding reservoirs of 0-2-2, 0-2-3, 0-2-4 sublayers in a multi-layer sandstone gas field completed by two-level subfacies modeling method phase model.

Four types of attribute modeling in this embodiment:

The three attribute models of porosity, gas saturation and permeability are established by the reservoir facies control method, and the basic principle is shown in formula (5); the gas layer quality classification attribute is directly generated through the above attribute model, and the basic principle is shown in formula (6). .

Reservoir facies-controlled three-attribute modeling

Modeling of gas layer quality classification attributes

In the formula: F—mapping; PROP _ij —single well property data; fg—sequential Gaussian stochastic simulation algorithm; RE—reservoir facies model; MPROP _j —property model; fff—gas layer Classification attribute parameter standard; GCLASS—gas reservoir quality classification attribute model; j=1 for porosity, 2 for gas saturation, 3 for permeability; m—number of wells.

Figures 4-6 respectively show the porosity, gas saturation and permeability of sublayers 0-2-2, 0-2-3 and 0-2-4 in a multi-layer sandstone gas field established by the method of reservoir facies control rate model, as well as the gas field quality classification standard based on porosity and gas saturation parameters of PetroChina Qinghai Oilfield Company (Table 1, Shi Qiang, et al, 2000; MaJianhai, 2008; Zhao Yan, et al, 2009), relying on the porosity model and the gas saturation model to directly establish a 3D model for gas layer quality classification.

Table 1. Gas layer quality classification parameter standard in a multi-layer sandstone gas field

fluid type POR S g Remark Type I atmosphere ＞=25% ＞=60% Aerosphere Type II atmosphere 25%>POR>=18%% 60%>Sg>=50%% medium atmosphere Type III atmosphere 18%>POR>=12% 60%>Sg>=50% Poor atmosphere

This specific implementation method has been applied in the quality classification and characterization of multi-layer sandstone gas reservoirs in Sebei Gas Field, Zhongba Gas Field, Sulige Gas Field, etc. in western my country, and has brought good social and economic benefits.

The basic principles and main features of the present invention and the advantages of the present invention have been shown and described above. Those skilled in the industry should understand that the present invention is not limited by the above-mentioned embodiments. What are described in the above-mentioned embodiments and the description only illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will also have Variations and improvements are possible, which fall within the scope of the claimed invention. The protection scope of the present invention is defined by the appended claims and their equivalents.

Claims (1)

1. A six-stage modeling method for the classification and characterization of gas layers in multi-layer sandstone gas reservoirs, characterized in that the method steps include: (A) Two-level structure modeling: the basic principles of two-level structure modeling are shown in formulas (1) and (2). The first level is stratum structure modeling. , bottom elevation data pair W _i , through the Kriging deterministic modeling algorithm f, to establish the top and bottom structure model S of the formation; the second level is sand body structure modeling, relying on the established top and bottom structure models Constrained by S, using the sand body top and bottom elevation data W _ij obtained from sand body division and comparison at m well points, and using the Kriging deterministic modeling algorithm f, to establish the top and bottom of n sand bodies in the formation , the bottom structure model S _j , to realize the distribution prediction of formations and sand bodies in three-dimensional space; Level 1: Stratigraphic Modeling The second level: sand body structure modeling In the formula: F——is mapping. (B) Two-level secondary facies modeling: the basic principles of two-level secondary facies modeling are shown in formulas (3) and (4). The first level is sandbody microfacies modeling, and the plane distribution of sandbody microfacies is directly used The graph is established by the Kriging deterministic modeling algorithm f; the second stage is reservoir facies modeling, the input data is the single well reservoir distribution data obtained by single well reservoir identification, and sequential indicator simulation or indicator g The Ligin stochastic simulation algorithm ff establishes the reservoir facies model; during the establishment process, the reservoir facies model is always placed under the constraints of the sand body microfacies model, so that the reservoir facies between well points can only randomly walk in the sand body. In the airspace limited by the microphase; Level 1: Sand body microfacies modeling Level 2: Reservoir Facies Modeling In the formula: F——mapping; GSF——a data set based on the digitalization of sand body microfacies distribution map drawn by geologists; SF——established sand body microfacies model; WRE—single well reservoir facies Data set; RE—reservoir facies model established; m—number of wells; (C) Modeling of four types of attributes: the three attribute models of porosity, gas saturation and permeability are established by the reservoir facies control method, and the basic principle is shown in formula (5); the gas layer quality classification attribute model is directly passed through the above attributes Model generation, the basic principle is shown in formula (6), Reservoir facies-controlled three-attribute modeling Modeling of gas layer quality classification attributes In the formula: F—mapping; PROP _ij —single well property data; fg—sequential Gaussian stochastic simulation algorithm; RE—reservoir facies model; MPROP _j —property model; fff—gas layer Classification attribute parameter standard; GCLASS—gas reservoir quality classification attribute model; j=1 for porosity, 2 for gas saturation, 3 for permeability; m—number of wells.