CN114439459B - SAGD yield prediction method and device - Google Patents
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Abstract
The invention relates to the technical field of oil reservoir yield prediction, in particular to a SAGD yield prediction method and device, wherein the method comprises the following steps: acquiring a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode; determining node data of each stage of yield change based on the yield prediction model; predicting average daily oil yield of each stage based on the node data of each stage of the yield change; based on the average daily oil production of each stage and node data of each stage, predicting the total oil production of a target oil reservoir in a preset time period, and predicting the oil production of each stage, wherein the oil production change trend of each stage is different, so that a prediction result of the total oil production in the preset time period is obtained, and the reliability of the prediction result is improved.
Description
Technical Field
The invention relates to the technical field of oil reservoir yield prediction, in particular to a SAGD yield prediction method and device.
Background
The recovery ratio is the most important comprehensive index for measuring the development effect and the development level of the oil field, the current prediction means is mainly a static method, and the method is only applicable to new areas and units with shorter exploitation time, and the dynamic method is better than the static method and can be applicable to the middle and later stages of development.
Some achievements are achieved in the existing recovery ratio prediction research, industry standards are established, but only an oil reservoir numerical simulation method in the industry standards is suitable for SAGD recovery ratio prediction, the certainty and the reliability of the oil reservoir numerical simulation method depend on the accuracy of oil reservoir geological modeling and history fitting, two conditions of verification of mining dynamic history fitting of a geological model, development schemes or support of development conceptual design are required to be met, meanwhile, the size of the model is limited by software and hardware operation capacity and operation duration, the fineness of the model is influenced by network step length, and therefore, a full oil reservoir model is difficult to build, or the whole process of three-field change and steam cavity formation expansion failure is difficult to describe more accurately, and the method has certain limitation.
Disclosure of Invention
The present invention has been made in view of the above problems, and is directed to providing a SAGD production prediction method and apparatus that overcomes or at least partially solves the above problems.
In a first aspect, the present invention provides a method for predicting SAGD production, comprising:
acquiring a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode;
determining node data of each stage of yield change based on the yield prediction model;
Predicting average daily oil production of each stage based on the node data of each stage of the yield change;
And predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production amount of each stage and the node data of each stage.
Further, after predicting the total oil production amount of the target oil reservoir in a preset period based on the average daily oil production amount of each stage and the node data of each stage, the method further comprises:
Obtaining the total oil production amount of the target oil reservoir in a preset time period and the total reserve of the target oil reservoir;
and determining the recovery ratio of the target oil reservoir based on the total oil production amount and the total reserve in the preset time period.
Further, the reservoir production prediction model comprises:
a vertical well horizontal well combined production prediction model and a double horizontal well production prediction model.
Further, the yield prediction model of the target reservoir includes a yield prediction model corresponding to each stage of a yield change, the each stage of the yield change including: a rising phase, a stabilizing phase and a damping phase, wherein yield prediction models respectively corresponding to the rising phase and the damping phase are different.
Further, the various stages of the yield change include: a rise phase, a steady phase, and a decay phase, the determining, based on the yield prediction model, individual phase node data for yield variation, comprising:
At the end of preheating, determining a first duration to an initial node of the rising phase and first daily oil production data at the initial node;
Determining a second duration from the initial node to a start node of the stable phase and second daily oil production data at the start node when a height of the steam cavity is equal to a reservoir thickness above a horizontal section of the horizontal well based on the yield prediction model;
Determining a third time period from a start node of the stable phase to an end node of the stable phase and third day oil production data at the end node based on the yield prediction model and the second day oil production data, wherein the second day oil production data is equal to the third day oil production data;
determining fourth daily oil production data for a terminal node during the decay phase and a fourth time period from the terminal node to the terminal node based on the daily oil production near a limiting oil/gas ratio and the yield prediction model.
Further, the determining fourth daily oil production data of the end node in the decay phase and a fourth time period from the end node to the end node based on the daily oil production when approaching the limit oil gas ratio and the yield prediction model comprises
Determining fourth daily oil production data of the terminal node in the attenuation stage based on the daily oil production when the limiting oil gas ratio is approaching;
a fourth time period from the end node to the end node is determined based on the fourth daily oil production data and the yield prediction model.
Further, the predicting average daily oil production for each stage based on the node data for each stage of the production variation includes:
based on the node data of each stage of the yield change, obtaining a functional relation between the time and the yield of each stage through fitting;
based on the functional relation between the time and the yield of each stage, obtaining a functional relation of average daily oil yield of each stage;
and predicting the average daily oil yield of each stage based on the functional relation of the average daily oil yield of each stage and the node data of each stage.
Further, the predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production amount of each stage and the node data of each stage includes:
Acquiring the oil production of a preset time period of each stage based on the average daily oil production of each stage and the node data of each stage;
And predicting the total oil production amount of the preset time period of the target oil reservoir based on the oil production amount of the preset time period of each stage.
Further, after predicting the total oil production amount in the preset time period of the target oil reservoir based on the average daily oil production amount in each stage and the node data in each stage, the method further includes:
and analyzing and evaluating the development effect or determining the oil production index of the preset time period in the development scheme based on the predicted total oil production amount of the target oil reservoir in the preset time period.
Further, the yield prediction model of the target oil reservoir specifically comprises the following steps: correcting the original yield prediction model based on the oil reservoir parameters of the target oil reservoir;
The method for correcting the original yield preset model based on the oil reservoir parameters of the target oil reservoir comprises the following steps:
the original yield prediction model is modified based on the relationship between the available head coefficients in the reservoir parameters of the target reservoir and the expansion angle of the steam cavity, and the effective length of the horizontal well.
In a second aspect, the present invention also provides a SAGD yield prediction apparatus, including:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring a well pattern combination mode of a target oil reservoir and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode;
The determining module is used for determining node data of each stage of yield change based on the yield prediction model;
The first prediction module is used for predicting average daily oil production of each stage based on the node data of each stage of the yield change;
And the second prediction module is used for predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production of each stage and the node data of each stage.
In a third aspect, the present invention also provides a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above method steps when executing the program.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the above method steps.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
The invention provides a method for predicting SAGD yield, which comprises the following steps: acquiring a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode; determining node data of each stage of yield change based on the yield prediction model; predicting average daily oil yield of each stage based on the node data of each stage of the yield change; based on the average daily oil production of each stage and node data of each stage, predicting the total oil production of a target oil reservoir in a preset time period, and predicting the oil production of each stage, wherein the oil production change trend of each stage is different, so that a prediction result of the total oil production in the preset time period is obtained, and the reliability of the prediction result is improved.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also throughout the drawings, like reference numerals are used to designate like parts. In the drawings:
FIG. 1 is a schematic flow chart of the steps of a SAGD yield prediction method according to an embodiment of the present invention;
FIG. 2 illustrates a vertical cross-section of a steam-reservoir interface in an embodiment of the invention;
FIG. 3 shows a vapor-liquid interface effusion material balance relationship diagram in an embodiment of the invention;
FIG. 4 shows a schematic cross-sectional view of a vapor chamber in an embodiment of the invention;
FIG. 5 shows a schematic overall flow chart of the predicted oil production in an embodiment of the invention;
FIG. 6 is a schematic diagram showing the q-t variation of the rise phase and decay phase according to a logarithmic function variation in an embodiment of the invention;
FIGS. 7a-7d are schematic diagrams illustrating piecewise fitting of rising phases in an embodiment of the present invention;
FIG. 8 shows a table of single well group production prediction results for a reservoir in an embodiment of the present invention;
FIG. 9 is a graph showing the predicted results plotted as a change in annual oil production in an embodiment of the present invention;
FIG. 10 shows a schematic structural diagram of a SAGD production prediction apparatus according to an embodiment of the present invention;
FIG. 11 shows a schematic structural diagram of a computer device for implementing the SAGD yield prediction method in an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example 1
A first embodiment of the present invention provides a method for predicting SAGD production, as shown in FIG. 1, comprising:
s101, acquiring a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode;
S102, determining node data of each stage of yield change based on a yield prediction model;
S103, predicting average daily oil yield of each stage based on node data of each stage of yield change;
s104, predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production amount of each stage and the node data of each stage.
In particular embodiments, it is desirable to select an appropriate yield prediction model. Therefore, a set of perfect oil reservoir engineering theory is established for SAGD technology at present, on the basis of fully utilizing the early research results, the effective pressure head correlation coefficient beta and the steam cavity expansion angle gamma are introduced, and the h Cavity(s) -q-t yield prediction model related to the effective pressure head correlation coefficient beta and the steam cavity expansion angle gamma is established. And then according to the oil reservoir and the existing data conditions, the existing yield prediction model can be utilized to predict the yield and the recovery ratio.
Firstly, a well pattern combination mode of a target oil reservoir is obtained, and a yield prediction model of the target oil reservoir is determined based on the well pattern combination mode.
For example, a dual horizontal well production prediction model is determined for a dual horizontal well pattern combination, and a vertical well + horizontal well production prediction model is determined for a vertical well + horizontal well pattern combination.
The yield prediction model is described in detail below:
1. double horizontal well yield prediction model
According to the vertical sectional view of the steam-oil reservoir interface and the vapor-liquid interface leakage material balance relation diagram as shown in fig. 2 and 3, and the relation formulas of a-h, the daily oil production formulas corresponding to the steam cavity rising and expanding (yield rising) stage and the daily oil production formulas of the steam cavity gradually decaying (decreasing) stage in the dual-horizontal well yield prediction model are obtained through correction of the model.
Wherein, the relation of a to h comprises:
a: darcy's law:
b: conservation of energy at the interface:
c: interface side temperature profile:
d: viscosity temperature empirical equation:
e: integrating the flow equation:
f: material balance equation at interface:
g: and (3) a drainage rate equation on one side of the horizontal well:
h: the corresponding steam front equation:
Wherein K is the effective permeability of the oil phase and the unit is mD; l is the length of the horizontal well section, and the unit is m; ρ 0,ρg is crude oil, steam density, kg/m 2, respectively; the angle theta is an included angle in the horizontal direction of the gas-liquid interface, and the unit is degree; mu 0 is the dynamic viscosity of crude oil in mPas; v s is the crude oil kinematic viscosity at steam temperature, the unit is the temperature of m 2/s;Ts steam, T R is the original temperature of the oil reservoir, and m is the coefficient of dimensionless viscosity Wen Xiangguan and the constant; u is interface speed, and the unit is m/s; ρc is the reservoir volume heat capacity in J/(m 3. Cndot.); xi is the normal distance between the steam and the interface, and the unit is m; lambda is the thermal conductivity of the oil reservoir, and the unit is w/(m DEG C); alpha is the thermal diffusivity of the oil layer, and the unit is m 2/s; phi is the porosity of the oil layer, and the unit is; Δs 0 is the movable oil saturation at steam temperature, f; h is the thickness of the oil layer above the horizontal section.
In the model modification, as shown in fig. 4, a schematic cross-sectional view of the steam chamber is shown. Because the pressure head h is only partially used for pushing the raw oil water to the production well in the SAGD production process, a coefficient beta related to the effective pressure head is introduced, and the oil drainage speeds at two sides of the horizontal production well are as follows:
The above is an original yield prediction model that has several drawbacks. Therefore, the original production prediction model needs to be modified, so that the production prediction model of the target oil reservoir is obtained.
Specifically, the original production prediction model is modified based on the relationship between the available head coefficient β in the reservoir parameters of the target reservoir and the expansion angle λ of the steam, and the effective length L i of the horizontal well.
Assuming that the shape of the steam cavity in the rising process is kept similar, the accumulated oil yield is in direct proportion to the product of movable oil in unit area and the square of the height of the steam cavity, and assuming that the expansion angle of the steam cavity is determined by the shape of the steam cavity, the area of the steam cavity is gamma h Cavity(s) 2, then the method comprises the following steps:
differentiating it with respect to time to obtain
Q=2γΦΔs 0h Cavity(s) 2, combined with formula (1-17) and integrated:
thus, the functional relation between the height h of the steam cavity and time is obtained:
in the formula, beta is an available pressure head coefficient, gamma is an expansion angle of a steam cavity, and the unit is radian; t is the production time, and the unit is d; Irrespective of reservoir properties, time, can be given by the target reservoir modulus or numerical results.
Thus, a staged production prediction model is obtained, comprising a steam chamber rise and expansion (production rise) stage:
by combining (1-9), (1-10), (1-12) The daily oil production at the rising stage is obtained as follows:
when the height of the steam cavity is equal to the thickness of the oil layer above the horizontal section, q basically does not change with time, daily oil yield basically reaches a peak value, and the stable oil yield stage is entered; the relationship between the height of the steam cavity and the production time is shown in the following formula (1-12).
Daily oil production calculation in the gradual exhaustion (decrease) stage of the steam cavity:
And obtaining the formula model corresponding to the dual horizontal well yield prediction model in the rising stage and the attenuation stage respectively.
The following is a vertical well + horizontal well combined production prediction model:
During the steam chamber rising phase:
In the straight-flat well combination, in order to ensure the development effect, the steam injection at the toe part is required to be ensured, and a relatively obvious end effect is generated, so that the oil drainage speed of the limited-length horizontal well is higher than that of the horizontal well part with the same length as that of the wireless long horizontal well, the end effect is equivalent to a crack, and the yield equation of the limited-length horizontal well by (1-13) and considering the end effect is as follows:
Wherein L e is the length for horizontal well, and the length is m.
From its dimensionless formAndThe method can obtain:
if the number of the steam injection wells is N, then
Wherein L i is the effective length of the horizontal well, and the unit is m.
The daily oil production calculation in the gradual attenuation (decrease) stage of the steam cavity can be obtained by the formula (1-14).
The yield prediction models for the double horizontal wells are shown as formulas (1-13) and (1-14), and the yield prediction models for the vertical wells and the horizontal wells are shown as formulas (1-17) and (1-14).
Reservoir parameters referred to in the present invention include: thermal diffusivity of oil layer, porosity, permeability, physical properties of crude oil, viscosity-temperature curve of crude oil, original oil saturation, oil saturation before SAGD conversion, oil layer thickness, steam injection temperature, steam injection dryness, horizontal section length, oil layer thickness above horizontal section and effective pressure head.
The thermal diffusivity, porosity, permeability, crude oil physical properties, original oil saturation, and crude oil viscosity-temperature curve of the oil layer are substantially unchanged for a determined oil reservoir.
For an individual SAGD well group, reservoir thickness, fang Youceng on horizontal section thickness, horizontal section length, are essentially unchanged, but different well group parameters are different.
The current oil saturation can be obtained by means of monitoring data, oil reservoir engineering calculation, oil reservoir numerical simulation and the like.
The movable oil saturation Δs o in the model formula is related to the steam cavity temperature, which in turn is related to the steam cavity pressure, the steam temperature in the cavity, the dryness and the like.
The property change of the injected steam can be obtained through software simulation calculation such as TWBS, CMG and the like so as to determine the temperature and pressure conditions of the bottom hole/steam cavity under different injection conditions and further obtain DeltaS o (movable oil saturation).
The coefficient beta can be obtained by the early production and monitoring data
The present invention exemplifies two reservoir yield prediction models, including: the vertical well horizontal well combines a production prediction model with a dual horizontal well production prediction model. Of course, there are other predictive models, and this is not an example.
After determining the yield prediction model, S102 is performed to determine the individual stage node data of the yield change based on the yield prediction model.
Regardless of the prediction model, the corresponding trend of the yield is an upward trend, then a stable region and finally a decay trend. Wherein, the yield prediction model corresponding to the rising stage of the rising trend and the attenuation stage of the attenuation trend are different.
In the dual horizontal well yield prediction model, the yield prediction model corresponding to the ascending phase is shown as formulas 1-13, and the yield prediction model corresponding to the decaying phase is shown as formulas 1-14.
In the vertical well and horizontal well yield prediction models, yield prediction models corresponding to the rising stage are shown as formulas 1-17, and yield prediction models corresponding to the decay stage are shown as formulas 1-14.
Accordingly, based on the yield preset model, the individual stage node data of the yield change is determined, wherein the segment node comprises: initial value q 0(h Cavity(s) ,t0), steady-phase start node q Stability and stability 0(h Cavity(s) ,t Stability and stability 0), end node q d0(h Cavity(s) ,td0) of the end of the steady phase, and end node q e(h Cavity(s) ,te of the end of the decay phase).
The initial daily oil production q 0 is the first time period of the initial node of the rising phase and the first daily oil production data q 0 at the initial node at the end of warm-up.
The starting node of the stabilization phase is based on a yield prediction model, namely formulas 1-12 and 1-13, and when the height of the steam cavity is equal to the thickness of the oil layer above the horizontal section of the horizontal well, the second time period from the starting node to the starting node of the stabilization phase is determined, and the second daily oil yield data q Stability and stability of the starting node is obtained.
Wherein the second time period is the time obtained according to formulas 1-12, subtracted from the first time period.
The end node of the stable phase is based on the yield prediction model, equations 1-14, and the second daily oil yield data, determining a third time period from the start node of the stable phase to the end node of the stable phase, and the third daily oil yield data at the end node, wherein the second daily oil yield data is equal to the third daily oil yield data.
Wherein the third time period is obtained by subtracting the second time period from the time obtained according to formulas 1 to 14.
The end node of the decay phase is configured to determine fourth daily oil production data q e for the end node during the decay phase and a fourth end node-to-end node duration based on the daily oil production and production prediction model when approaching the limiting gas-oil ratio.
Since the daily oil production data at the approaching the limiting oil/gas ratio is known, this fourth daily oil production data q e is obtained.
Next, a fourth end node to end node duration is determined based on the fourth daily oil production data and the yield prediction model, equations 1-14.
According to formulas 1-14, under the condition that the fourth day oil production data is known, obtaining the time length reaching the terminal node, subtracting the third time length from the time length, and obtaining the second time length, wherein the first time length is the fourth time length.
Thus, each stage node data is obtained.
The node data of each stage can be checked and corrected, and will not be described in detail here.
Next, S103 is executed to predict the average daily oil production for each stage based on the node data for each stage of the production variation.
Specifically, based on node data of each stage of the yield change, obtaining a functional relation between time and yield of each stage through fitting; obtaining a functional relation of average daily oil yield of each stage based on the functional relation between time and yield of each stage; based on the functional relationship of the average daily oil production of each stage and the node data of each stage, the average daily oil production of each stage is predicted.
Because the production time unit in the prediction model is d, the annual oil production is predicted in practical application, the unnecessary calculated amount is reduced, the fitting precision is ensured, and at least three value time points between the (i+1) th year and the (i) th year are required to be met when q-t are calculated, wherein t i+1-ti = constant R, namely, the time interval is equal, and the annual time q-t is not less than 3 points.
The q-t curves of the two stages of yield rise and attenuation show remarkable monotonicity and concave-convex performance, so that the yield change of the two stages is determined to be consistent with the exponential/logarithmic function change in the middle of the basic elementary function, namely the function form after fitting is q (t) =kln (t) +m or q (t) =ke mt.
The fitted function is determined by fitting to determine the k value, the m value.
Then, the daily yield needs to be converted from the annual yield.
The calculated q-t values and the plotted curves are instantaneous values with the units of t/d (daily oil production) to d (time), and the method for carrying out the aging is as follows:
For the yield rising stage and the attenuation stage, integrating the determined piecewise function by a definite integral median theorem to obtain:
Thereby obtaining average daily oil production in a period of time The integration interval [ a, b ] is the production time starting and ending point of the required year, and the upper integration limit of the previous interval is the lower limit of the next integration interval for the adjacent interval, so the average daily oil yield formula of each phase is predicted as follows:
Or (b)
The values of k and m in the average daily oil production formula for each stage can be determined by the values of k and m in the fitting function.
For the stabilization phase, the daily oil production in the stabilization phase is basically the peak period yield, namely the average daily oil production in the stabilization phase is expressed as
Therefore, after the formula for predicting the average daily oil production of each stage is obtained, S104 is performed to predict the total amount of the preset time period of the target reservoir based on the average daily oil production of each stage and the node data of each stage.
The total oil production amount for the preset time period can be specifically the total annual oil production amount, the total quaternary oil production amount or the total oil production amount in an individual month. The following describes the total annual oil production:
specifically: acquiring the oil production of a preset time period of each stage based on the average daily oil production of each stage and the node data of each stage; and predicting the total oil yield (total annual oil yield) of the target oil reservoir in the preset time period based on the oil yield of the preset time period of each stage.
For the rising phase and the decay phase, the annual oil production of the rising phase or the decay phase is obtained asWherein b-a corresponds to the second duration of the ascent phase and the fourth duration of the decay phase, so that the annual oil production of the ascent phase and the annual oil production of the decay phase are predicted from the node data of each phase of the production variation and the average daily oil production of each phase.
Of course, oil production in one quarter, etc. may also be predicted, and will not be described in detail herein.
For the stabilization phase, the annual oil production thus obtained for the stabilization phase isIf the influence of time rate needs to be considered and the production time interval is consistent with the two stages of yield rising and attenuation, the production time rate coefficient is introducedHere, (b-a) is based on the last year (b-a) ue of the yield-increasing stage, and thus the annual oil yield of the stationary stage
After the annual oil production of each stage is obtained, the total annual oil production of the target reservoir is predicted based on the annual oil production of each stage.
Specifically, the annual oil production of the SAGD stage of the single well group is determined, and the annual oil production is accumulated to obtain the total annual oil productionWherein, n is the production age, the total annual production of the whole oil reservoir can be obtained in two ways:
one is to predict the output of each single well group by using the output prediction model and accumulate;
the other is to import the parameters related to the typical well group into the production prediction model, and expand the production of the typical well group to the whole oil field according to the parameters.
After predicting the total annual oil production amount of the target oil reservoir, verifying and correcting the predicted result, specifically drawing an annual oil production change curve from the predicted result, observing whether abnormal points exist in the form of the curve, if so, returning to S102, and re-determining the abnormal points in the determination of the node data of each stage of the yield change so as to adjust and eliminate the abnormal points, thereby ensuring the smoothness of the annual oil production change curve.
After determining that the predicted total oil production amount of the target oil reservoir within the preset time period is accurate, the method further comprises the following steps:
obtaining the total oil yield of a target oil reservoir in a preset time period; and determining the recovery ratio of the target oil reservoir based on the total oil production amount and the total reserve in the preset time period.
Specifically, the recovery equation
Wherein EOR SAGD is recovery ratio, Q is total oil production in a preset time period, and N Reserve volume is total oil reservoir reserves.
In a specific embodiment, taking a certain oil reservoir as an ultra-heavy oil reservoir as an example, predicting the total annual oil production amount, and describing the method by combining the whole flow chart of the invention as shown in a figure 5, wherein the average porosity of the oil reservoir is 23% -26%, and the permeability is 1200mD-1600mD, and the method belongs to a high-pore high-permeability oil reservoir; a vertical well and horizontal well combined yield prediction model is selected, wherein the well spacing of the horizontal wells is 70m, the vertical wells are positioned among the horizontal wells, the horizontal well section is positioned at the lower part of the target layer, the length of the horizontal section is 300-400 m, the thickness of an oil layer above the horizontal section is 40-45 m, and the SAGD area controls geological reserves to be 157 multiplied by 10 4 t.
And (5) according to the selected diameter and horizontal well combination yield prediction model. Determining, from the yield prediction model, each stage node data for a yield change, comprising: the initial node q 0、t0 is calculated by the formulas (1-12) and (1-17), and q 0＝9.13t/d,t0 =30d.
The end of the yield-up phase, i.e. the start node q Stability and stability 0、t Stability and stability 0 of the stabilization phase, where qstability 0 =38.36 t/d, tstability 0 =1200d when h Cavity(s) =h.
The initial node of the stable stage is the end of the rising stage, and the highest stage value can be increased by 5-10% compared with the end of the rising stage according to the mine experience when the production time rate is considered.
The initial value of the decay phase, i.e., the end value q d0、td0 of the stabilization phase, corresponds to the daily oil production obtained through formulas (1-17) corresponding to q ue, where t d0 =3000 d.
For the end node q e、te of the decay phase, first, by the daily yield of q e =4.6t/d approaching the limit fuel-air ratio, the corresponding time t e =3700 d is obtained according to the formula (1-14).
Thus, the q-t change coincidence logarithmic function change of the ascending phase and the decaying phase is obtained according to the above-mentioned selected vertical well + horizontal well combination yield prediction model and the determined node data of each phase as shown in fig. 6.
Then, the annual daily yields of the yield-increasing phase and the decay phase are obtained, and the annual daily yield of the steady phase is given by the rise-phase end yield x R ηi.
The annual daily output in the rising phase, the decay phase and the steady phase can be obtained as follows:
1. And directly solving by a phase fitting function, substituting the annual production time as an integral upper limit and an integral lower limit into a corresponding fitting formula, and integrating to obtain the annual average daily oil production.
2. And (3) taking the years as a basic unit, subdividing the intervals, re-fitting and providing a q-t function expression of each year, substituting the annual production time as an integral upper limit and an integral lower limit into a corresponding fitting formula, and integrating to obtain the annual oil production. Specifically, as shown in fig. 7a to 7d, a piecewise fitting curve is used in the ascending phase, and fitting functions of each interval in the yield attenuation phase can be obtained in the same way, and meanwhile, the annual average daily oil yield can be obtained.
The annual average daily yield obtained by the method is the product of the annual average daily yield and the production time, namely the annual yield. Compared with expert approval results, the annual errors and the final errors are less than 10%, so that the requirements of field application are met.
As shown in fig. 8, a table of the single well group production prediction results for this reservoir is shown.
Then, the prediction result is drawn into an annual oil production change curve, as shown in fig. 9, the curve form and whether abnormal points exist are observed, if so, the method returns to S102, the stage node data of each stage is determined, and the calculation of the abnormal point data is repeated to eliminate the abnormal points, so that the curve is relatively smooth.
Finally, in the determination of recovery ratio, when the substituted parameter is a single well group parameter, the annual oil reservoir yield can be accumulated by the single well group prediction result; when the modern parameter is a typical well group parameter of the target area, the annual oil deposit yield is the product of the predicted result and the number of well groups, and in the embodiment, the proxy parameter is the typical well group parameter, so that the annual oil deposit yield recovery ratio of the target area is 30.86% -31.08%, and the recovery ratio of expert approval is 30.94%, and the reliability of the predicted result is higher.
After obtaining the predicted total oil production amount of the target oil reservoir in the preset time period, the method further comprises the following steps: and analyzing and evaluating the development effect or compiling the development scheme based on the predicted total oil production amount of the target oil reservoir in a preset time period.
Application of predicting oil production:
index predictions in various scheme compilations are in units of years.
In the analysis and evaluation of the development effect, annual indexes are used as the basis; the indexes such as annual oil production, oil-gas ratio, oil production speed, oil extraction and injection ratio are generally annual index and stage index, and the calculation of stage index is generally annual node.
One or more technical solutions in the embodiments of the present invention at least have the following technical effects or advantages:
The invention provides a method for predicting SAGD yield, which comprises the steps of obtaining a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode; determining node data of each stage of yield change based on the yield prediction model; predicting average daily oil yield of each stage based on the node data of each stage of the yield change; based on the average daily oil production of each stage and node data of each stage, predicting the total oil production of a target oil reservoir in a preset time period, and predicting the oil production of each stage, wherein the oil production change trend of each stage is different, so that a prediction result of the total oil production in the preset time period is obtained, and the reliability of the prediction result is improved.
Example two
Based on the same inventive concept, the invention also provides a device for predicting SAGD yield, as shown in FIG. 10, comprising:
an obtaining module 1001, configured to obtain a well pattern combination manner of a target oil reservoir, and determine a yield prediction model of the target oil reservoir based on the well pattern combination manner;
a determining module 1002, configured to determine node data of each stage of the yield change based on the yield prediction model;
a first prediction module 1003, configured to predict average daily oil production of each stage based on node data of each stage of the yield change;
And a second prediction module 1004, configured to predict a total oil production amount in a preset time period of the target oil reservoir based on the average daily oil production amount in each stage and the node data in each stage.
In an alternative embodiment, the system further comprises a recovery prediction module for:
Obtaining the total oil production amount of the target oil reservoir in a preset time period and the total reserve of the target oil reservoir;
and determining the recovery ratio of the target oil reservoir based on the total oil production amount and the total reserve in the preset time period.
In an alternative embodiment, the reservoir production prediction model comprises:
a vertical well horizontal well combined production prediction model and a double horizontal well production prediction model.
In an alternative embodiment, the yield prediction model of the target reservoir includes a yield prediction model corresponding to each stage of yield variation, the each stage of yield variation including: a rising phase, a stabilizing phase and a damping phase, wherein yield prediction models respectively corresponding to the rising phase and the damping phase are different.
In an alternative embodiment, the stages of the yield change include: a rising phase, a stabilizing phase, and a decay phase, the determining the model 1002 includes:
a first determining subunit, configured to determine, when warm-up is over, a first duration to an initial node of the rising stage and first daily oil production data at the initial node;
A second determining subunit, configured to determine, based on the yield prediction model, a second duration from the initial node to a start node of the stable phase when a height of the steam cavity is equal to a thickness of the oil layer above the horizontal section of the horizontal well, and second daily oil yield data at the start node;
A third determining subunit configured to determine, based on the yield prediction model and the second daily oil production data, a third duration from a start node of the stable phase to an end node of the stable phase, and third daily oil production data at the end node, where the second daily oil production data is equal to the third daily oil production data;
A fourth determination subunit configured to determine fourth daily oil production data of a terminal node in the decay phase and a fourth duration from the terminal node to the terminal node based on the daily oil production when the limiting oil/gas ratio is approaching and the yield prediction model.
In an alternative embodiment, the fourth determining subunit is configured to:
Determining fourth daily oil production data of the terminal node in the attenuation stage based on the daily oil production when the limiting oil gas ratio is approaching;
a fourth time period from the end node to the end node is determined based on the fourth daily oil production data and the yield prediction model.
In an alternative embodiment, the first prediction module 1003 includes:
The first obtaining unit is used for obtaining a functional relation between time and yield of each stage through fitting based on the node data of each stage of the yield change;
the obtaining unit is used for obtaining a functional relation of average daily oil yield of each stage based on the functional relation between the time and the yield of each stage;
And the first prediction subunit is used for predicting the average daily oil production of each stage based on the functional relation of the average daily oil production of each stage and the node data of each stage.
In an alternative embodiment, the second prediction module 1004 includes:
The second obtaining module is used for obtaining the oil production of a preset time period of each stage based on the average daily oil production of each stage and the node data of each stage;
And the second prediction subunit is used for predicting the total oil production amount of the preset time period of the target oil reservoir based on the oil production amount of the preset time period of each stage.
In an alternative embodiment, the method further comprises: and the application module is used for analyzing and evaluating the development effect or determining the oil production index of the preset time period in the development scheme based on the predicted total oil production amount of the preset time period of the target oil reservoir.
In an alternative embodiment, the yield prediction model of the target oil reservoir is specifically: correcting the original yield prediction model based on the oil reservoir parameters of the target oil reservoir;
the method for correcting the original yield prediction model based on the oil reservoir parameters of the target oil reservoir comprises the following steps:
the original yield prediction model is modified based on the relationship between the available head coefficients in the reservoir parameters of the target reservoir and the expansion angle of the steam cavity, and the effective length of the horizontal well.
Example III
Based on the same inventive concept, an embodiment of the present invention provides a computer device, as shown in fig. 11, including a memory 1104, a processor 1102, and a computer program stored in the memory 1104 and capable of running on the processor 1102, where the processor 1102 implements the steps of the SAGD yield prediction method described above when executing the program.
Where in FIG. 11a bus architecture (represented by bus 1100), bus 1100 may include any number of interconnected buses and bridges, with bus 1100 linking together various circuits, including one or more processors, represented by processor 1102, and memory, represented by memory 1104. Bus 1100 may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., all of which are well known in the art and, therefore, will not be described further herein. Bus interface 1106 provides an interface between bus 1100 and receiver 1101 and transmitter 1103. The receiver 1101 and the transmitter 1103 may be the same element, i.e. a transceiver, providing a means for communicating with various other apparatus over a transmission medium. The processor 1102 is responsible for managing the bus 1100 and general processing, while the memory 1104 may be used to store data used by the processor 1102 in performing operations.
Example IV
Based on the same inventive concept, a fourth embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the SAGD production prediction method described above.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in a SAGD production prediction device, computer device, according to an embodiment of the present invention. The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (9)
1. A method for predicting SAGD production comprising:
acquiring a well pattern combination mode of a target oil reservoir, and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode;
determining node data of each stage of yield change based on the yield prediction model;
Predicting average daily oil production of each stage based on the node data of each stage of the yield change;
Predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production amount of each stage and the node data of each stage;
the predicting average daily oil production of each stage based on the node data of each stage of the yield change comprises the following steps:
based on the node data of each stage of the yield change, obtaining a functional relation between the time and the yield of each stage through fitting;
based on the functional relation between the time and the yield of each stage, obtaining a functional relation of average daily oil yield of each stage;
Predicting the average daily oil yield of each stage based on the functional relation of the average daily oil yield of each stage and the node data of each stage;
The various stages of the yield change include: a rise phase, a steady phase, and a decay phase, the determining, based on the yield prediction model, individual phase node data for yield variation, comprising:
At the end of preheating, determining a first duration to an initial node of the rising phase and first daily oil production data at the initial node;
Determining a second duration from the initial node to a start node of the stable phase and second daily oil production data at the start node when a height of the steam cavity is equal to a reservoir thickness above a horizontal section of the horizontal well based on the yield prediction model;
Determining a third time period from a start node of the stable phase to an end node of the stable phase and third day oil production data at the end node based on the yield prediction model and the second day oil production data, wherein the second day oil production data is equal to the third day oil production data;
Determining fourth daily oil production data for a terminal node at the decay phase and a fourth time period from the terminal node to the terminal node based on the daily oil production near a limiting oil/gas ratio and the yield prediction model;
The determining of fourth daily oil production data of the end node in the decay phase and a fourth time period from the end node to the end node based on the daily oil production when approaching the limit oil gas ratio and the yield prediction model comprises
Determining fourth daily oil production data of the terminal node in the attenuation stage based on the daily oil production when the limiting oil gas ratio is approaching;
Determining a fourth duration from the end node to the end node based on the fourth daily oil production data and the yield prediction model;
the yield prediction model of the target oil reservoir specifically comprises the following steps: correcting the original yield prediction model based on the oil reservoir parameters of the target oil reservoir;
The method for correcting the original yield preset model based on the oil reservoir parameters of the target oil reservoir comprises the following steps:
the original yield prediction model is modified based on the relationship between the available head coefficients in the reservoir parameters of the target reservoir and the expansion angle of the steam cavity, and the effective length of the horizontal well.
2. The method of predicting as set forth in claim 1, further comprising, after said predicting a total amount of oil produced for a predetermined period of time of said target reservoir based on said average daily oil production for each stage and said each stage node data:
Obtaining the total oil production amount of the target oil reservoir in a preset time period and the total reserve of the target oil reservoir;
and determining the recovery ratio of the target oil reservoir based on the total oil production amount and the total reserve in the preset time period.
3. The prediction method of claim 1, wherein the target reservoir production prediction model comprises:
a vertical well horizontal well combined production prediction model and a double horizontal well production prediction model.
4. The prediction method of claim 1, wherein the yield prediction model of the target reservoir comprises yield prediction models corresponding to respective phases of yield change, wherein the yield prediction models corresponding to the ascending phase and the decaying phase, respectively, are different.
5. The prediction method of claim 1, wherein predicting the total amount of oil produced in the target reservoir for a preset period of time based on the average daily oil production for each stage and the node data for each stage comprises:
Acquiring the oil production of a preset time period of each stage based on the average daily oil production of each stage and the node data of each stage;
And predicting the total oil production amount of the preset time period of the target oil reservoir based on the oil production amount of the preset time period of each stage.
6. The method of predicting as set forth in claim 1, wherein said predicting the total amount of oil produced for a predetermined period of time of said target reservoir based on said average daily oil production for each stage and said each stage node data further comprises:
and analyzing and evaluating the development effect or compiling the development scheme based on the predicted total oil production amount of the target oil reservoir in a preset time period.
7. A SAGD production prediction apparatus for implementing the method of any one of claims 1-6, comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring a well pattern combination mode of a target oil reservoir and determining a yield prediction model of the target oil reservoir based on the well pattern combination mode;
The determining module is used for determining node data of each stage of yield change based on the yield prediction model;
The first prediction module is used for predicting average daily oil production of each stage based on the node data of each stage of the yield change;
And the second prediction module is used for predicting the total oil production amount of the target oil reservoir in a preset time period based on the average daily oil production of each stage and the node data of each stage.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method steps of any of claims 1-6 when the program is executed.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method steps of any of claims 1-6.
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