Ultrathin-layer common heavy oil reservoir depletion type development productivity prediction method

Abstract

The invention discloses an ultra-thin layer common heavy oil reservoir depletion type development productivity prediction method which comprises the following steps: performing component phase state simulation on crude oil of a target heavy oil reservoir, splitting the crude oil into three crude oil pseudo-components, namely a light crude oil pseudo-component, a medium crude oil pseudo-component and a heavy crude oil pseudo-component, and determining corresponding parameter data; establishing a three-dimensional geologic model of the target area; using the parameter data and the three-dimensional geologic model to perform depletion type development numerical simulation operation to obtain data of oil production and output crude oil components changing along with time; calculating the economic limit daily oil production; when the daily oil production of depletion type development is reduced to the daily oil production of the economic limit, production is stopped, and the economic validity period of depletion type development of the heavy oil reservoir and the oil production and the recovery ratio in the economic validity period are obtained according to a numerical simulation result; the method solves the problems that no method for predicting the depletion-type development productivity of the ultrathin-layer heavy oil reservoir exists, and depletion-type development of the heavy oil reservoir cannot be accurately guided.

Description

Technical Field

[0001] The present invention relates to the technical field of heavy oil development, and in particular to a method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Heavy oil generally refers to crude oil with high viscosity. According to my country's petroleum and natural gas industry standards, the viscosity of degassed crude oil is 100-10000mPa·s at the original reservoir temperature or the density is 934-1000kg / m at 15.6℃ and atmospheric pressure. 3 Crude oil includes heavy oil, high viscosity oil, oil sands, natural asphalt and oil shale. As conventional crude oil development is limited by reserve growth, heavy oil development plays an increasingly important role in making up for the shortage of conventional crude oil production.

[0004] Table 1 China's heavy oil classification standards

[0005]

[0006] According to the current classification standard of heavy oil in my country (see Table 1), heavy oil is divided into four categories with viscosity as the main index and density as the auxiliary index. The second category of ordinary heavy oil and extra heavy oil and super heavy oil are developed by thermal recovery. Thermal recovery technology includes steam huff and puff and steam flooding technology.

[0007] Steam stimulation is a method of production in which a certain amount of steam is injected into an oil well periodically, the well is soaked, and then the well is opened to produce oil. Steam stimulation technology is suitable for reservoirs with poor inter-well connectivity, high crude oil viscosity, asphaltene, and those that are not suitable for steam drive. Since steam stimulation production is a single-well operation and relies on natural energy for production, it can only produce crude oil in the oil layer near each oil well, leaving a large amount of dead oil zone between wells, and the general recovery rate is only 10% to 20%. With the development of steam injection technology, steam stimulation is no longer regarded as an important stage, but only as an important auxiliary measure in the steam drive process. The screening conditions for heavy oil reservoirs suitable for steam stimulation include reservoir burial depth, crude oil viscosity, total effective thickness of oil layer, net total thickness ratio of oil layer, reserve coefficient, etc. Table 2 is the basic principles for screening heavy oil reservoirs with steam stimulation in China.

[0008] Table 2 Screening criteria for heavy oil steam stimulation production in China

[0009]

[0010] Note: Crude oil viscosity refers to the viscosity of degassed crude oil at reservoir temperature.

[0011] Steam flooding is a method of production in which high-temperature wet steam is continuously injected into the oil layer by injection wells to heat and displace crude oil, which is then produced by production wells. Steam flooding technology is suitable for shallow reservoirs with high viscosity and low relative density, where the crude oil viscosity is less than 50,000 mPa·s, the depth is less than 1,600 m, the residual oil saturation of the reservoir is high, and the geological structure is simple. The spacing between the injection well and the production well is 100 to 150 m smaller than the general spacing. Since the 1970s, the production of steam injection development in the world has been rising continuously, accounting for more than 60% of the total thermal recovery EOR production. Heavy oil reservoirs suitable for steam flooding are restricted by reservoir depth, sandstone thickness, net-to-total ratio of oil layers, porosity, permeability, oil saturation and plane connectivity. Table 3 is the principle of steam flooding screening for heavy oil reservoirs at home and abroad.

[0012] Table 3 Principles of steam flooding screening for heavy oil reservoirs at home and abroad

[0013]

[0014]

[0015] According to the current steam stimulation screening standards in my country, the effective thickness of heavy oil reservoirs suitable for steam stimulation development should be greater than 5m. If the effective thickness is less than 5m, it cannot be developed by steam stimulation. According to the current steam drive screening standards at home and abroad, the effective thickness of heavy oil reservoirs suitable for steam drive development should be greater than 6m. If the effective thickness is less than 6m, it cannot be developed by steam drive.

[0016] According to the heavy oil development experience at home and abroad, the average crude oil viscosity of the target area heavy oil reservoir at the original reservoir temperature is 250mpa.s, and thermal recovery should be adopted. However, the average effective thickness of the target area is 3.5m, which belongs to the ultra-thin layer of ordinary heavy oil reservoir, and is at the technical and economic lower limit of thermal recovery. Therefore, in order to reduce the development cost, only depletion recovery can be carried out.

[0017] However, there is currently no experience to draw on for the depletion-type development of ultra-thin heavy oil reservoirs at home and abroad, and there is no capacity prediction method for the depletion-type development of ultra-thin heavy oil reservoirs. During the depletion-type development of ordinary ultra-thin heavy oil reservoirs, the crude oil has high viscosity and poor fluidity. The conventional numerical simulation capacity prediction method cannot accurately simulate the real seepage characteristics of crude oil in the oil layer, so it is impossible to accurately predict the capacity prediction method for the depletion-type development of ultra-thin heavy oil reservoirs.

[0018] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may contain information that does not constitute prior art. Summary of the invention

[0019] In view of at least one of the above technical problems, the present disclosure provides a method for predicting the production capacity of ultra-thin ordinary heavy oil reservoirs by depletion-type development, which solves the problem that there is no method for predicting the production capacity of ultra-thin heavy oil reservoirs by depletion-type development, and cannot accurately guide the depletion-type development of such heavy oil reservoirs.

[0020] In order to achieve the above-mentioned invention object, the method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir by depletion development is characterized by comprising:

[0021] Performing component phase simulation on the crude oil in the target heavy oil reservoir, splitting the crude oil into three crude oil pseudo-components of light, medium and heavy, and determining corresponding parameter data;

[0022] Establish a three-dimensional geological model of the target area;

[0023] Using the parameter data and the three-dimensional geological model to perform numerical simulation calculations for exhaustion development, and obtaining data on oil production and output crude oil components changing over time;

[0024] Calculate the economic limit daily oil production;

[0025] When the daily oil production of depletion-type development is reduced to the economic limit daily oil production, production is stopped. According to the numerical simulation results, the economic effective period of depletion-type development of the heavy oil reservoir, the oil production and the recovery factor within the economic effective period are obtained.

[0026] In the present disclosure and possible embodiments, when performing the component phase simulation, the molecular weight, density, boiling point and viscosity-temperature curve parameter data of the target heavy oil reservoir oil sample are input into the feed to obtain the mole percentage, molecular weight, critical pressure, critical temperature and viscosity-temperature curve parameter data of the three crude oil pseudo-components.

[0027] In the present disclosure and possible embodiments, when performing the component phase simulation, the light crude oil component is defined as C9H 20 , molecular weight is 0.128259 kg / gmole, critical pressure is 2310 kPa, critical temperature is 321.45 ° C; the medium crude oil is simulated as ordinary heavy oil, and the component is defined as C 15 H 32 , molecular weight is 0.212421kg / gmole, critical pressure is 1520kPa, critical temperature is 433.85℃; the heavy crude oil is pseudo-super heavy oil, and the component is defined as C 20 H 42 , molecular weight is 0.282556kg / gmole, critical pressure is 1115kPa, and critical temperature is 493.85℃.

[0028] In the present disclosure and possible embodiments, when performing the component phase simulation, the relationship between the initial K value and the critical temperature and critical pressure of the component is defined as:

[0029] LqCy i =5.37(1+W i )(1-T ci / T)+ln(P ci / P);

[0030] Among them, K i is the equilibrium constant of component i; W i is the eccentricity factor of component i; T ci is the critical temperature of component i, °C; T is the temperature, °C; P ci is the critical pressure of component i; P is the pressure, kPa; and / or,

[0031] When calculating the mutual influence relationship between pseudo-components, the relationship between the mutual influence coefficient and the critical volume of the component is defined as:

[0032] δ ij =[(2V ci 1 / 6 V cj 1 / 6 ) / (V ci 1 / 3 +V cj 1 / 3 )] θ ;

[0033] Among them, δ ij is the influence coefficient between pseudo-components; V ci is the critical volume of component i; V cj is the critical volume of component j, v 3 ; θ is the hydrocarbon interaction coefficient index; and / or,

[0034] When calculating the viscosity-temperature curve of each pseudo-component, the relationship between the pseudo-component and the original oil sample viscosity is defined as:

[0035]

[0036] Among them, μ o is the viscosity of the target heavy oil reservoir oil sample, mPa.s; μ oi is the pseudo-component viscosity, mPa.s; n is the number of pseudo-components; and / or,

[0037] The component phase simulation was performed using the WINPROP module of CMG software.

[0038] In the present disclosure and possible embodiments, a three-dimensional geological model of the target area is established using PETREL geological modeling software;

[0039] The geological model includes reservoir attribute distribution, reservoir porosity, permeability, oil saturation and net-to-gross ratio model;

[0040] By using the geological, logging and seismic data parameters of completed wells, combined with regional sedimentary characteristics, after data discretization, the kriging deterministic modeling method is used for internal interpolation to form a reservoir attribute distribution model and establish reservoir porosity, permeability and oil saturation models; based on the well point data, a net-to-gross ratio model is established.

[0041] In the present disclosure and possible embodiments, reserve calculation is performed based on the reservoir attribute distribution model, the reservoir porosity model, the permeability model, and the oil saturation model; reserve fitting is performed based on the net-to-gross ratio model, and the reserve fitting accuracy is set to be above 95%; and / or,

[0042] The geological, logging and seismic data parameters include point data and surface data. The point data includes well information and well point attributes, and the surface data includes plan views and structural views.

[0043] In the present disclosure and possible embodiments, the reservoir property distribution model, the reservoir porosity model, the permeability model, and the oil saturation model are exported into a BIN format and input into the STARS module of the CMG numerical simulation software for the numerical simulation.

[0044] In the present disclosure and possible embodiments, the initial temperature and initial pressure corresponding to the reservoir depth in the initial state, the production well working system and the set production time are input into the CMG numerical simulation software;

[0045] When performing the numerical simulation, the component property data segment includes a water phase and an oil phase. The water phase is selected as H2O from the component library, and the oil phase is the parameter data of the three crude oil pseudo-components derived from the phase state simulation. The input value of the water phase viscosity is 0, and the automatic activation program uses the default value for water; relative permeability data is imported into the rock fluid data segment.

[0046] In the present disclosure and possible embodiments, the production well operation system includes a minimum bottom hole flow pressure and a maximum liquid production, and the production time is set to 10 years; and / or,

[0047] Before performing the simulation operation, the parameter results of the grid output include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, and the oil production and accumulation of the layer;

[0048] The parameter results of the grid output in the software result file RESULTS module include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, the oil production and accumulation of the layer.

[0049] In the present disclosure and possible embodiments, the break-even principle is applied, and the daily output when the daily income is equal to the daily expenditure is the economic limit daily oil production; the specific formula is as follows:

[0050] The income for the day is: P o ×q o ×α;

[0051] The expenditure for the day is: C e +q o ×α×R×P o ;

[0052] Income = Expenditure: P o ×q o ×α=Ce+q o ×α×R×P o ;

[0053]

[0054] Where: Q o is the economic limit daily oil production, yuan; C e is the daily operating cost of a single well, RMB; α is the commodity rate of crude oil; P o is the crude oil price, RMB / t; R is the comprehensive tax rate, %; q o is the daily oil production, t / d.

[0055] The present disclosure has the following beneficial effects:

[0056] The capacity prediction method for depletion-type development of ultra-thin ordinary heavy oil reservoirs disclosed in the present invention artificially splits the crude oil in the heavy oil reservoir into three crude oil pseudo-components of light, medium and heavy, simulates the real seepage process of the heavy oil in the formation during the depletion-type development process, thereby accurately predicting the economic effective period of the depletion-type development of the heavy oil reservoir, the oil production and the recovery rate within the economic effective period, providing technical guidance for the depletion-type development of such ultra-thin ordinary heavy oil reservoirs, and effectively solving the problem that in the depletion-type development process of ultra-thin ordinary heavy oil reservoirs, due to the high viscosity and poor fluidity of the crude oil, the conventional numerical simulation capacity prediction method cannot accurately simulate the real seepage characteristics of the crude oil in the oil layer, and therefore cannot accurately predict the capacity of the depletion-type development of ultra-thin heavy oil reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0058] Figure 1 is a viscosity-temperature curve diagram of an embodiment of the present disclosure;

[0059] Figure 2 is the oil-water relative permeability curve at different temperatures of the embodiment of the present disclosure;

[0060] Figure 3 is a graph showing changes in pseudo components of crude oil in the near-wellbore zone over time according to an embodiment of the present disclosure;

[0061] Figure 4 is a daily oil production curve predicted by numerical simulation of exhaustion-type development according to an embodiment of the present disclosure;

[0062] Figure 5 is a curve of predicted daily oil production and actual daily oil production of exhaustion-type development output according to an embodiment of the present disclosure;

[0063] Figure 6 It is a curve of predicted recovery rate of depletion-type development production and actual production recovery rate in the embodiment of the present disclosure. DETAILED DESCRIPTION

[0064] The present disclosure is described below based on embodiments, but it is worth noting that the present disclosure is not limited to these embodiments. In the detailed description of the present disclosure below, some specific details are described in detail. However, for the parts that are not described in detail, those skilled in the art can also fully understand the present disclosure.

[0065] In addition, those skilled in the art should understand that the drawings are only provided to illustrate the purpose, features and advantages of the present disclosure, and the drawings are not actually drawn to scale.

[0066] At the same time, unless the context clearly requires otherwise, the words "include", "comprises" and similar words throughout the specification and claims should be interpreted as inclusive rather than exclusive or exhaustive; that is, the meaning is "including but not limited to".

[0067] Example

[0068] The disclosed embodiment uses the disclosed method to predict the production capacity of a target heavy oil reservoir through depletion development. The specific process is as follows:

[0069] Step 1: Collection of basic data for numerical simulation and economic limit calculation of target heavy oil reservoir.

[0070] Collect heavy oil sample data, including: crude oil molecular weight, density, boiling point, viscosity-temperature curve, relative permeability, daily operating cost of a single well, crude oil commodity rate, crude oil price, and comprehensive tax rate parameters.

[0071] The molecular weight of crude oil is 0.176316 kg / gmole and the density is 0.954 g / cm 3 , boiling point 321℃, viscosity-temperature curve Figure 1 , oil-water relative permeability curve is shown in Figure 2 The daily operating cost of a single well is 1,415 yuan / ton; the commodity rate of crude oil is 99.48%; the price of crude oil is 2,299 yuan / ton.

[0072] tons; the comprehensive tax rate is 43.16% (including 25% income tax; 13% crude oil value-added tax; 6% crude oil resource tax, 40% resource tax reduction for heavy oil and high-viscosity oil; urban construction and maintenance tax is calculated based on the turnover tax amount, with a tax rate of 7%; education tax is calculated based on the turnover tax amount, with a tax rate of 5%).

[0073] The viscosity of degassed crude oil at the original reservoir temperature in this target area is 250mpa.s.

[0074] Step 2: Component phase simulation calculation.

[0075] The embodiment of the present disclosure uses the WINPROP phase state program in the commercial numerical simulation software CMG, and uses the conventional technical means in the field to perform component phase state simulation, input the molecular weight, density, boiling point, and viscosity-temperature curve parameter data of the oil sample in the feed; perform component splitting operation, select C9H 20 , C 15 H 32 , C 20 H 42 Type I (pseudo-light crude oil) refers to ordinary light crude oil, and its components are defined as C9H 20 ; Type II (quasi-ordinary heavy oil), component definition is C 15 H 32 ; Type III (quasi-super heavy oil), component definition is C 20 H 42 .

[0076] In this embodiment, the component phase simulation involves the following characterization methods:

[0077] (1) Type I (pseudo-light crude oil) refers to ordinary light crude oil, and its components are defined as C9H 20 , molecular weight is 0.128259kg / gmole, critical pressure is 2310kPa, critical temperature is 321.45℃; Type II (quasi-ordinary heavy oil), component definition is C 15 H 32 , molecular weight is 0.212421kg / gmole, critical pressure is 1520kPa, critical temperature is 433.85℃; Type III (pseudo-super heavy oil), component definition is C 20 H 42, molecular weight is 0.282556

[0078] kg / gmole, the critical pressure is 1115kPa, and the critical temperature is 493.85℃. The specific component phase simulation results are shown in Table 1 and Table 2:

[0079] Table 1 Crude oil component splitting results

[0080]

[0081]

[0082] Table 2 Viscosity-temperature data of three pseudo-components

[0083] Temperature(℃) Lightweight components Middle quality components Heavy components 10 200.2 656.3 4048.7 66 7.9 19.3 84.0 121 1.5 2.9 9.7 177 0.6 0.9 2.6 233 0.4 0.6 1.1 289 0.2 0.4 0.6 344 0.2 0.2 0.4 400 0.2 0.2 0.2

[0084] (2) To start the calculation, merge into C9H 20 , C 15 H 32 , C 20 H 42 Three pseudo-components, the relationship between the initial K value and the critical temperature and critical pressure of the component is defined as:

[0085] LqCy i =5.37(1+W i )(1-T ci / T)+ln(P ci / P);

[0086] Among them, K i is the equilibrium constant of component i; W i is the eccentricity factor of component i; T ci is the critical temperature of component i, °C; T is the temperature, °C; P ci is the critical pressure of component i; P is the pressure, kP.

[0087] (3) In order to calculate the mutual influence relationship between pseudo-components, the relationship between the mutual influence coefficient and the critical volume of the component is defined as:

[0088] δ ij =[(2V ci 1 / 6 V cj 1 / 6 ) / (V ci 1 / 3 +V cj 1 / 3 )] θ ;

[0089] Among them, δ ij is the influence coefficient between pseudo-components; V ci is the critical volume of component i; Vcj is the critical volume of component j, v 3 ; θ is the hydrocarbon mutual influence coefficient index;

[0090] (4) In order to calculate the viscosity-temperature curve of each pseudo-component, the relationship between the pseudo-component and the original oil sample viscosity is defined as:

[0091]

[0092] Among them, μ o is the viscosity of the target heavy oil reservoir oil sample, mPa.s; μ oi is the pseudo-component viscosity, mPa.s; n is the number of pseudo-components.

[0093] Step 3: Establish a 3D geological model of the heavy oil reservoir.

[0094] The three-dimensional geological model of the heavy oil reservoir in the embodiment of the present disclosure is established using the commercial geological modeling software PETREL.

[0095] Using conventional technical means in this field, based on the geological, logging and seismic data of the completed wells in the target area, the parameters of the geological, logging and seismic data include point data and surface data, where the point data includes well information and well point attributes, and the surface data includes plane maps and structural maps. Combined with regional sedimentary characteristics, based on geostatistics theory, after data discretization, the kriging deterministic modeling method is used for internal interpolation to form a reservoir attribute distribution model, and establish a reservoir porosity, permeability and oil saturation model.

[0096] According to the understanding of the temperature field in the oil layer during steam stimulation, the well network form has little effect on the steam stimulation effect within a certain well spacing range. In order to improve the simulation accuracy and facilitate the study of steam drive development effect, the plane grid width is 20m, and it is divided into three layers vertically, including the overlying interlayer and the underlying interlayer. The production layer is the oil layer of the S11 and S12 sublayers. In order to improve the simulation accuracy, the production layer is vertically subdivided into 6 grid systems. Reserve calculation is based on the reservoir model, porosity, and saturation parameter model established by three-dimensional modeling. At the same time, a net-to-gross ratio model was established based on the well point data.

[0097] In order to calculate the model reserve accuracy, the relationship between the net-to-gross volume and the rock pore volume is defined as:

[0098]

[0099]

[0100] HCPV=V pore ×(1-S w );

[0101]

[0102] Where: V net is the net-to-gross volume, m 3 ; V bulk is the total volume of rock, m 3 ; N is sandstone thickness, m; G is formation thickness, m; Φ is porosity; HCPV is underground crude oil volume, m 3 ; V pore is the rock pore volume, m 3 ; S W is water saturation, f; V oil is the volume of crude oil after degassing on the ground, m 3 ; B0 is the volume coefficient.

[0103] The reserve fitting accuracy reaches more than 95%, indicating that the model is consistent with the actual geological conditions. The disclosed method determines that the fitting accuracy is higher than 90% before the next step of numerical simulation calculation can be carried out.

[0104] The calculated model reserves are 84.77×10 4 t, the geological reserves of the block are 85×10 4 t, the reserve fitting accuracy is 99.7%, which meets the reserve fitting accuracy requirements.

[0105] Step 4: Design model components.

[0106] The structural model (reservoir attribute distribution model) and porosity, permeability, and oil saturation models generated by the PETREL geological model, which can accurately describe the characteristics of thin and narrow channel sand body heavy oil reservoirs and sand body distribution, are exported into BIN format and imported into the STARS module of the CMG numerical simulation software for production capacity prediction. In the definition of the component property data segment, water phase components are added, and the oil phase components are added as three pseudo-components derived from the phase state simulation; the viscosity-temperature curve is the viscosity-temperature curve data of the three pseudo-components derived from the phase state simulation, and the water phase viscosity is input as 0, and the automatic activation program uses the default value for water.

[0107] Import the phase permeability curve data in step (1) into the rock fluid data segment. Enter the following parameters into the CMG numerical simulation software: define the initial temperature corresponding to the reservoir depth in the initial state as 30°C and the initial pressure as 2.64MPa, define the production well working system (including the minimum bottom hole pressure of 1.5MPa and the maximum liquid production of 15m 3 ), define the production time as 10 years, and then perform numerical simulation calculations of exhaustion development.

[0108] Step 5: Conduct numerical simulation to predict oil production from depletion development.

[0109] Before the operation, the parameter results of the grid output are determined to include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, the oil production and accumulation of the layer.

[0110] The parameter results of the grid output in the RESULTS module of the CMG numerical simulation software result file include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, the oil production and accumulation of the layer, and charts are drawn based on the component and production data.

[0111] It can be seen that in the process of depletion development, the reservoir pressure continues to decline, the production capacity of the production wells decreases, and the light components in the crude oil are produced first. At 180 days of production, the heavy components in the near-wellbore area reach 100%, and the viscosity of the crude oil produced at the wellhead of the production well continues to increase. With the production of the light components of the crude oil near the wellbore area, the viscosity of the crude oil is getting higher and higher, the fluidity drops sharply, and the daily oil production decreases ( Figure 3 ).

[0112] Step 6: Calculate the economic limit daily oil production, clarify the economic validity period, oil production and recovery factor within the economic validity period.

[0113] According to the conventional technical means in this field, the economic limit daily oil production is calculated based on the daily operating cost of a single well, the oil commodity rate, the crude oil price, and the comprehensive tax rate to obtain the economic limit daily oil production of the target area. When the daily oil production of the depletion development is reduced to the economic limit daily oil production, the depletion development method is stopped. According to the numerical simulation results, the economic effective period of the depletion development, the oil production and recovery factor within the economic effective period are obtained.

[0114] Specifically, based on the above-mentioned relevant parameters, the following formula is used to obtain that when the oil price is 45 US dollars per barrel, the economic limit daily oil production is 1.09 tons. When the daily oil production of exhaustion development is reduced to the economic limit daily oil production, exhaustion development is stopped ( Figure 4 ). Using the break-even principle, the daily output when the daily income equals the daily expenditure is the economic limit daily oil production. The derivation process of the economic limit daily oil production formula is as follows:

[0115] The income for the day is: P o ×q o ×α;

[0116] The expenditure for the day is: C e +q o ×α×R×P o ;

[0117] Income = Expenditure: Po × qo × α = Ce + qo × α × R × Po;

[0118]

[0119] Where: Q o is the economic limit daily oil production, yuan; C e is the daily operating cost of a single well, RMB; α is the commodity rate of crude oil; P o is the crude oil price, RMB / t; R is the comprehensive tax rate, %; q o is the daily oil production, t / d.

[0120] The numerical simulation of depletion development predicts that when production reaches 367 days, the economic limit of daily oil production is 1.09 tons, the cumulative oil production is 727.7 tons, and the recovery rate is 3.3%. The actual production situation is that when production reaches 380 days, the economic limit of daily oil production is 1.09 tons, the cumulative oil production is 783.5 tons, and the recovery rate is 3.57%. The compliance rate of the capacity prediction method reached 93% ( Figure 5 , Figure 6 ).

[0121] The above-described embodiments are only embodiments of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present disclosure. It should be noted that, for a person of ordinary skill in the art, without departing from the concept of the present disclosure, several variations, equivalent substitutions, improvements, etc. may be made, all of which fall within the scope of protection of the present disclosure. Therefore, the scope of protection of the present disclosure shall be subject to the attached claims.

Claims

1. A method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development, characterized in that: include: Performing component phase simulation on the crude oil in the target heavy oil reservoir, splitting the crude oil into three crude oil pseudo-components of light, medium and heavy, and determining corresponding parameter data; Establish a three-dimensional geological model of the target area; Using the parameter data and the three-dimensional geological model to perform numerical simulation calculations for exhaustion development, and obtaining data on oil production and output crude oil components changing over time; Calculate the economic limit daily oil production; When the daily oil production of depletion-type development is reduced to the economic limit daily oil production, production is stopped. According to the numerical simulation results, the economic effective period of depletion-type development of the heavy oil reservoir, the oil production and the recovery factor within the economic effective period are obtained.

2. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 1 is characterized by: When performing the component phase simulation, the molecular weight, density, boiling point and viscosity-temperature curve parameter data of the target heavy oil reservoir oil sample are input into the feed to obtain the mole percentage, molecular weight, critical pressure, critical temperature and viscosity-temperature curve parameter data of the three crude oil pseudo-components.

3. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 2 is characterized by: When performing the component phase simulation, the light crude oil component is defined as C9H 20 , molecular weight is 0.128259 kg / gmole, critical pressure is 2310 kPa, critical temperature is 321.45 ° C; the medium crude oil is simulated as ordinary heavy oil, and the component is defined as C 15 H 32 , molecular weight is 0.212421kg / gmole, critical pressure is 1520kPa, critical temperature is 433.85℃; the heavy crude oil is pseudo-super heavy oil, and the component is defined as C 20 H 42 , molecular weight is 0.282556kg / gmole, critical pressure is 1115kPa, and critical temperature is 493.85℃.

4. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 3 is characterized by: When performing the component phase simulation, the relationship between the initial K value and the critical temperature and critical pressure of the component is defined as: lnK i =5.37(1+W i )(1-T ci / T)+ln(P ci / P); Among them, K i is the equilibrium constant of component i; W i is the eccentricity factor of component i; T ci is the critical temperature of component i, °C; T is the temperature, °C; P ci is the critical pressure of component i; P is the pressure, kPa; and / or, When calculating the mutual influence relationship between pseudo-components, the relationship between the mutual influence coefficient and the critical volume of the component is defined as: δ ij =[(2V ci 1 / 6 V cj 1 / 6 ) / (V ci 1 / 3 +V cj 1 / 3 )] θ ; Among them, δ ij is the influence coefficient between pseudo-components; V ci is the critical volume of component i; V cj is the critical volume of component j, v 3 ; θ is the hydrocarbon interaction coefficient index; and / or, When calculating the viscosity-temperature curve of each pseudo-component, the relationship between the pseudo-component and the original oil sample viscosity is defined as: Among them, μ o is the viscosity of the target heavy oil reservoir oil sample, mPa.s; μ oi is the pseudo-component viscosity, mPa.s; n is the number of pseudo-components; and / or, The component phase simulation was performed using the WINPROP module of CMG software.

5. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 4 is characterized by: Using PETREL geological modeling software, a three-dimensional geological model of the target area is established; The geological model includes reservoir attribute distribution, reservoir porosity, permeability, oil saturation and net-to-gross ratio model; By using the geological, logging and seismic data parameters of completed wells, combined with regional sedimentary characteristics, after data discretization, the kriging deterministic modeling method is used for internal interpolation to form a reservoir attribute distribution model and establish reservoir porosity, permeability and oil saturation models; based on the well point data, a net-to-gross ratio model is established.

6. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 5 is characterized by: Performing reserve calculation based on the reservoir attribute distribution model, the reservoir porosity model, the permeability model, and the oil saturation model; performing reserve fitting based on the net-to-gross ratio model, and setting the reserve fitting accuracy to be above 95%; and / or, The geological, well logging and seismic data parameters include point data and surface data, the point data includes well information and well point attributes, and the surface data includes plan views and structural maps; and / or, The reserve accuracy of the established model is above 90%. When calculating the reserve accuracy of the model, the relationship between the net-to-gross volume and the rock pore volume is defined as: HCPV=V pore ×(1-S w ); Where: V net is the net-to-gross volume, m 3 ; V bulk is the total volume of rock, m 3 ; N is sandstone thickness, m; G is formation thickness, m; Φ is porosity; HCPV is underground crude oil volume, m 3 ; V pore is the rock pore volume, m 3 ; S W is water saturation, f; V oil is the volume of crude oil after degassing on the ground, m 3 ; B0 is the volume coefficient.

7. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 6 is characterized by: The reservoir attribute distribution model, the reservoir porosity model, the permeability model and the oil saturation model are exported into BIN format and input into the STARS module of CMG numerical simulation software for the numerical simulation.

8. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 7 is characterized by: Input the initial temperature and initial pressure corresponding to the reservoir depth in the initial state, the production well working system and the set production time into the CMG numerical simulation software; When performing the numerical simulation, the component property data segment includes a water phase and an oil phase. The water phase is selected as H2O from the component library, and the oil phase is the parameter data of the three crude oil pseudo-components derived from the phase state simulation. The input value of the water phase viscosity is 0, and the automatic activation program uses the default value for water; relative permeability data is imported into the rock fluid data segment.

9. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 8 is characterized by: The production well working system includes the minimum bottom hole flow pressure and the maximum liquid production, and the production time is set to 10 years; and / or, Before performing the simulation operation, the parameter results of the grid output include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, and the oil production and accumulation of the layer; The parameter results of the grid output in the software result file RESULTS module include the component composition of all phases and the phase flow rate; the parameter results of the well output include the component / phase summary of the well, the component / phase summary of the output, the oil production and accumulation of the layer.

10. The method for predicting the production capacity of an ultra-thin layer common heavy oil reservoir depletion development according to claim 9 is characterized in that: Using the break-even principle, the daily output when the daily income equals the daily expenditure is the economic limit daily oil production; the specific formula is as follows: The income for the day is: P o ×q o ×α; The expenditure for the day is: C e +q o ×α×R×P o ; Income = Expenditure: P o ×q o ×α=Ce+q o ×α×R×P o ; Where: Q o is the economic limit daily oil production, yuan; C e is the daily operating cost of a single well, RMB; α is the commodity rate of crude oil; P o is the crude oil price, RMB / t; R is the comprehensive tax rate, %; q o is the daily oil production, t / d.

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