CN107327294A - A kind of porosity means of interpretation become based on fine and close oily reservoir under the conditions of matrix parameter - Google Patents

Abstract

A kind of porosity means of interpretation become based on fine and close oily reservoir under the conditions of matrix parameter, first, playback processing is carried out to fine and close oily reservoir core, effective fine and close oily reservoir core lacunarity analysis data, the oily reservoir CLAY MINERALS AND THEIR SIGNIFICANCE content of densification of the effective rock core of comparative analysis, rock constituents is selected（Mainly include chiltern, grey matter, white clouds matter）, porosity characteristics and its corresponding gamma ray log be with respect to value parameter（ΔGR）, acoustic travel time logging parameter（Δt）, density log parameter（ρ_b）, neutron well logging parameter（Φ_N）Response characteristic, ignore influence of the oiliness to log parameter in fine and close oily reservoir pore space, the porosity logging interpretation model become based on fine and close oily reservoir under the conditions of matrix parameter is set up, formation is a kind of to determine the porosity logging means of interpretation that fine and close oily reservoir becomes under the conditions of matrix parameter.This method is favorably improved fine and close oily reservoir porosity well log interpretation precision.

Description

A Porosity Interpretation Method Based on Variable Skeleton Parameters of Tight Oil Reservoirs

technical field

The invention belongs to the technical field of prospecting and logging, and in particular relates to a porosity interpretation method based on variable skeleton parameters of tight oil reservoirs.

Background technique

Tight oil refers to oil resources from tight reservoirs (siltstone, sandstone, limestone, dolomite, etc.) other than shale. At present, the United States has successfully achieved a breakthrough in the exploration and development of tight oil, showing good development prospects. Tight oil is also widely distributed in major oil and gas-bearing basins in China, mainly developing tight sandstone oil or tight carbonate oil that coexists or contacts with lacustrine source rocks and is widely distributed. Tight oil exploration is an important successor area for oil and gas exploration, but due to Due to the special reservoir geological background of tight oil formation, the reservoir rock composition is complex and variable, which leads to complex and variable rock skeleton parameters and is difficult to determine, which affects the accuracy of tight oil reservoir logging porosity prediction.

Well logging data is indispensable in evaluating the porosity parameters of tight oil, and the comprehensive evaluation of well logging has become the technical support for current tight oil exploration and development. The practice of tight oil exploration in China reveals that the content of clay minerals and rock components (including sandy, calcareous, dolomitic, etc.) The determination is difficult, which in turn affects the prediction accuracy of tight oil porosity. For example, the tight oil in the Zhahaquan Oilfield of the Qaidam Basin is mainly located in the IV Sha Formation of the lower member of the Shangganchaigou Formation. According to the porosity and X-ray diffraction analysis data of the reservoir cores of the IV sand group, the porosity is generally 0.3%-10.4% (mainly 2.0-6.5%), the content of clay minerals is generally 5.5%-49.9%, quartz, feldspar and rock The total clastic content is generally 12.5-77.9%, the calcareous content is generally 0.0%-58.1%, and the dolomitic content is generally 0.0%-48.8%. The lithology of tight oil reservoirs is mainly clastic rock, chemical rock and transitional types. Complex and changeable rock types will inevitably lead to changeable and difficult to determine logging skeleton parameters. Corresponding analysis of tight oil reservoir core porosity and logging acoustic time difference, density, and neutron logging parameters, based on the zero porosity skeleton parameter selection method, processing to obtain reservoir rock skeleton acoustic time difference, skeleton density, and skeleton neutron parameters They are 164～197μs/m (mainly 175～195μs/m), 2.65～2.83g/cm ³ (mainly 2.67～2.79g/cm ³ ), -2～12.0pu (mainly 2.5～9.0pu), Skeleton parameters vary widely with depth and are difficult to determine, which affects the prediction accuracy of porosity logging.

Therefore, it has become an urgent practical problem to fully consider the influence of the variation characteristics of reservoir framework parameters on logging parameters and to form a porosity interpretation method suitable for tight oil reservoirs under the condition of variable framework parameters.

Contents of the invention

In order to overcome the above-mentioned deficiencies in the prior art, the object of the present invention is to provide a porosity interpretation method based on the variable framework parameters of tight oil reservoirs. Porosity is one of the key parameters for evaluation of tight oil reservoirs. Porosity characteristics, starting from the practice of tight oil exploration and development, make full use of logging parameters to predict the porosity of tight oil reservoirs with variable skeleton parameters. A Porosity Interpretation Method Subject to Skeleton Parameters.

In order to achieve the above object, the technical scheme adopted in the present invention is:

A porosity interpretation method based on variable framework parameters of tight oil reservoirs, comprising the following steps:

1) First, homing the tight oil reservoir cores, selecting the effective porosity analysis data of the tight oil reservoir cores and performing core calibration, and correspondingly analyzing the clay mineral content and rock components in the effective tight oil reservoir cores ( Including sandy, calcareous, dolomitic), porosity parameter characteristics and their changes in the relative value parameters of natural gamma ray logging (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and neutron The corresponding response characteristics in the logging parameters (Φ _N );

2) Then, according to the principles of natural gamma ray logging, acoustic transit time logging, density logging, and neutron logging, the relative value parameter (ΔGR) of natural gamma ray logging is the shale content and sand content in tight oil reservoirs. , calcareous content, dolomitic content and their radioactive characteristics. Acoustic transit time logging parameters (Δt), density logging parameters (ρ _b ), and neutron logging parameters (Φ _N ) are all indicators of sand content in the reservoir. , calcareous content, dolomitic content, shale content, porosity and oiliness comprehensively reflect, ignoring the influence of oiliness in pores of tight oil reservoirs on acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and neutron The impact of logging parameters (Φ _N ), according to the relative value parameters of natural gamma ray logging (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and neutron logging parameters (Φ _N ), establish the functional relationship between the above logging parameters and the shale content, sand content, calcareous content, dolomitic content and porosity parameters in the reservoir, forming a combination of 4 logging response equations plus 1 logging volume The equations composed of model formulas are used to solve the shale content, sand content, calcareous content, dolomite content and porosity parameters in tight oil reservoirs, and then determine the porosity of tight oil reservoirs under the condition of variable skeleton parameters ;

Based on the multiple linear regression analysis method, the relative value parameters of natural gamma ray logging (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and medium Sub-logging parameters (Φ _N ), a logging model for comprehensive processing and interpretation of tight oil reservoir porosity (Φ), the specific formula is:

Φ＝0.067Δt-10.129ρ _b -0.122Φ _N -3.335ΔGR+19.696

In the formula, the correlation coefficient R=0.8040;

3) On the basis of step 1) and step 2), according to the clay mineral content, rock composition (including sandy, calcareous, dolomitic), and porosity parameters in the effective tight oil reservoir cores selected in step 1), , and the corresponding relative value parameters of natural gamma ray logging (ΔGR), sonic transit time logging parameters (Δt), density logging parameters (ρ _b ) and neutron logging parameters (Φ _N ), according to step 2) analysis Based on the principle, multivariate linear regression analysis was performed to establish a porosity logging interpretation model based on the variable skeleton parameters of tight oil reservoirs.

The beneficial effects of the present invention are:

1) The response characteristics of various parameters in conventional logging data to the porosity of tight oil reservoirs are fully considered to ensure the evaluation accuracy of tight oil porosity.

2) By implementing various steps of porosity logging evaluation of tight oil reservoirs, including core calibration, logging response characteristic analysis, data analysis, and logging interpretation model establishment, focusing on porosity parameter prediction under the condition of variable skeleton parameters, and finally improving Well logging evaluates the porosity accuracy of tight oil reservoirs, which is beneficial to the systematic evaluation of tight oil.

To sum up, this scheme helps to focus on the analysis of the logging response of tight oil reservoirs under the condition of variable framework parameters and improve the efficiency and accuracy of logging evaluation of tight oil porosity, which is beneficial to the systematic evaluation of tight oil.

detailed description

The present invention is further described below in conjunction with the examples, but the present invention is not limited to the following examples.

Example 1

Take the tight oil of the IV sand formation in the lower member of the Shangganchaigou Formation in the Zhahaquan oil area of the Qaidam Basin as an example.

In the first step, according to the clay mineral content, sand content, lime content and dolomite content in the core of the tight oil reservoir core of the tight oil reservoir core of the fourth sand group in the lower section of the Ganchaigou Formation in Well ZP1, the key exploration well for tight oil in the Zhahaquan oil area of the Qaidam Basin , porosity analysis data, after core homing processing, select 510 effective tight oil reservoir core porosity analysis test data, correspondingly analyze the natural gamma ray relative value parameter (ΔGR) and acoustic time difference measurement in the logging data of Well ZP1 Well parameters (Δt), density logging parameters (ρ _b ) and neutron logging parameters (Φ _N ) characteristics.

In the second step, using the core porosity analysis parameters of tight oil reservoirs and the corresponding natural gamma ray logging parameters (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and neutron logging parameters Well parameters (Φ _N ) were analyzed by linear regression and multiple linear regression respectively. The core porosity of tight oil reservoirs was correlated with acoustic transit time logging parameters (Δt), density logging parameters (ρ _b ), and neutron logging parameters respectively. (Φ _N ) The correlation coefficients of individual parameters are 0.066, 0.554 and 0.463, respectively. Due to the influence of variable framework parameters of tight oil reservoirs, the correlation is generally poor. Based on the multiple linear regression analysis method, the relative value parameters of natural gamma ray logging (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and medium Sub-logging parameters (Φ _N ), a logging model for comprehensive processing and interpretation of tight oil reservoir porosity (Φ), the specific formula is:

Φ＝0.067Δt-10.129ρ _b -0.122Φ _N -3.335ΔGR+19.696

In the formula, the correlation coefficient R=0.8040.

It can be seen that the comprehensive use of natural gamma ray logging parameters (ΔGR), acoustic time difference logging parameters (Δt), density logging parameters (ρ _b ) and neutron logging parameters (Φ _N ) to interpret tight oil reservoirs The porosity and correlation coefficient meet the requirements of relevant industries.

Through the above steps, a porosity interpretation method based on the variable framework parameters of tight oil reservoirs is formed, which is helpful to improve the porosity prediction accuracy of tight oil reservoirs, and further facilitates the systematic evaluation of tight oil reservoirs.

Claims (1)

1. a kind of porosity means of interpretation become based on fine and close oily reservoir under the conditions of matrix parameter, it is characterised in that including following Step：

1) playback processing first, is carried out to fine and close oily reservoir core, effective fine and close oily reservoir core lacunarity analysis money is selected Expect and carry out drilling core graduation, the effective fine and close oily reservoir core CLAY MINERALS AND THEIR SIGNIFICANCE content of correspondence analysis, rock constituents (including sand Matter, calcium carbonate, white clouds matter), porosity parameter attribute and its change gamma ray log with respect to value parameter (Δ GR), sound wave when Poor log parameter (Δ t), density log parameter (ρ_b) and neutron well logging parameter (Φ_N) in corresponding response characteristic；

2) then, according to gamma ray log, acoustic travel time logging, density log, neutron well logging principle, gamma ray log phase It is shale content, sandy content, calcium carbonate content, white clouds matter content and its radioactivity in fine and close oily reservoir to value parameter (Δ GR) The concentrated expression of feature, acoustic travel time logging parameter (Δ t), density log parameter (ρ_b), neutron well logging parameter (Φ_N) it is storage Sandy content, calcium carbonate content, white clouds matter content, shale content, porosity and oiliness concentrated expression in layer, ignore fine and close oil Oiliness is to acoustic travel time logging parameter (Δ t), density log parameter (ρ in reservoir pore space_b) and neutron well logging parameter (Φ_N) Influence, respectively according to gamma ray log with respect to value parameter (Δ GR), acoustic travel time logging parameter (Δ t), density log parameter (ρ_b) and neutron well logging parameter (Φ_N), set up above-mentioned each log parameter and contain with shale content, sandy content, calcium carbonate in reservoir Functional relation between amount, white clouds matter content and porosity parameter, forms and adds 1 well logging volume mould by 4 log response equations The equation group that type formula is collectively formed, contains to the shale content in fine and close oily reservoir, sandy content, calcium carbonate content, white clouds matter Amount and porosity parameter are solved, and then determine the oily reservoir porosity of densification under the conditions of change matrix parameter；

With multiple linear regression analysis method, the gamma ray log become based on fine and close oily reservoir under the conditions of matrix parameter is set up With respect to value parameter (Δ GR), acoustic travel time logging parameter (Δ t), density log parameter (ρ_b) and neutron well logging parameter (Φ_N), it is comprehensive The logging module that fine and close oily reservoir porosity (Φ) is explained in processing is closed, specific formula is：

The Δ t-10.129 ρ of Φ=0.067_b-0.122Φ_N-3.335ΔGR+19.696

In formula, coefficient R=0.8040；

3) in step 1) and step 2) on the basis of, according to step 1) in select clay pit in effective fine and close oil reservoir core Thing content, rock constituents (including chiltern, calcium carbonate, white clouds matter), porosity parameter, and corresponding gamma ray log phase To value parameter (Δ GR), acoustic travel time logging parameter (Δ t), density log parameter (ρ_b) and neutron well logging parameter (Φ_N), according to Step 2) analysis principle, multiple linear regression analysis processing is carried out, sets up and is become based on fine and close oily reservoir under the conditions of matrix parameter Porosity logging interpretation model.