CN111079341B - Intelligent well completion and oil reservoir unsteady state coupling method based on iterative algorithm - Google Patents
Intelligent well completion and oil reservoir unsteady state coupling method based on iterative algorithm Download PDFInfo
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Abstract
An intelligent well completion and oil reservoir unsteady state coupling method based on an iterative algorithm comprises the following steps of firstly, acquiring parameters required by coupling simulation; step two, before coupling iteration, determining an initial state; and step three, performing coupling iterative operation at any time step T ═ T, and step four, when comparing the time step T ═ T, the pressure and flow of the well-containing grid respectively calculated by the oil reservoir simulator and the well completion simulator, realizing unsteady simulation of coupling flow in an iterative mode based on the independently operated oil reservoir simulator and well completion simulator, fully considering the mutual influence between the intelligent well completion and the oil reservoir and unsteady flow characteristics generated by intelligent well completion regulation and control, realizing unsteady coupling flow simulation in a development period by iterating at each time step and stepping the iteration result according to time, accurately simulating and predicting the development dynamics of the oil reservoir under intelligent well completion dynamic regulation and control, and providing a basis for further optimizing regulation and control and improving the development efficiency.
Description
Technical Field
The invention relates to a well completion oil reservoir simulation method in the field of oil and gas field development, in particular to a well completion oil reservoir unsteady-state coupling method based on an iterative algorithm.
Background
The intelligent well completion is applied to a Snorre offshore platform in Norway for the first time in 1997, and the good application effect of the intelligent well completion is valued by oil and gas companies at home and abroad and is widely popularized in oil fields. Over twenty years of development, intelligent well completion has gradually become one of the revolutionary technologies that have changed traditional production systems and reservoir development management. Intelligent completions were first commonly used in offshore field development, accounting for approximately 70% of the total. Nowadays, intelligent well completion is widely applied to various types of new and old oil fields, and covers various complex situations of different operation environments (sea, land, desert and the like), different oil reservoir types (sandstone and carbonate) and different development well types (vertical wells, horizontal wells, multilateral wells, extended reach wells and the like). The underground controllability of the intelligent well completion provides great convenience for oil and gas field development and oil reservoir management, but the purposes of optimizing production dynamics, improving oil reservoir management capability and improving final recovery ratio by reasonably regulating and controlling the underground control device are not easy. In fact, optimal regulation is a complex, dynamic, systematic process. The intelligent well completion is utilized to carry out scheme design and real-time regulation and control of oil and gas field development, and the most important basis is real-time simulation and dynamic prediction of well completion and oil reservoir. From different angles, many scholars explore various coupling methods of well completion and reservoir simulation. In the existing research, CN 201610978645.1 discloses a horizontal well geological optimization design method under the condition of coupling a shaft and an oil reservoir, which mainly uses a target oil reservoir to perform fine oil reservoir description and store a fine geological model to continuously correct and perfect the oil reservoir model, and then performs optimization design according to the design specifications and requirements of the horizontal well. Chinese patent CN 201410638037.7 discloses a fracture-reservoir-shaft coupling model, and for input parameters, the coupling model is subjected to full implicit difference by using an N-R iteration method, so that the parameters of a reservoir, a fracture and a shaft are solved simultaneously. Chinese patent CN201910062416.9 discloses a method for simulating the distribution rule of parameters such as pressure, temperature and steam inflow in an oil reservoir and steam pressure, temperature and dryness along the well bore in the development process of a double-tube SAGD horizontal well by coupling a well bore model and a reservoir model based on the pressure and temperature continuity principle, and solving the model by adopting a full implicit finite difference method and an iteration technology. In the 01 th year 2004, the pressure and flow distribution under the condition of coupling between the shaft and the oil reservoir were solved by using the analytic simultaneous equations provided earlier by the institute of Petroleum, Ching of southwest, Chuangang, and the like. 2011, volume 33, phase 06, oil drilling and production process, luoyuan et al, discussed a coupling method for simultaneous reservoir seepage and wellbore flow by using an analytic method. In 2011, volume 33, stage 05, an oil drilling and production process, Zelin et al indicate that the accuracy of analytical calculation is far lower than the yield obtained after grid subdivision is carried out on a horizontal well under the condition of considering oil reservoir seepage coupling, and a steady-state coupling method of well completion and oil reservoir seepage is provided for describing the pressure distribution of a horizontal well shaft and the yield prediction of segmented well completion. In 2015, volume 39, at 06 th, the university of Chinese Petroleum journal (Nature science edition), Chenyang et al introduced the concept of quasi-conductivity to establish an analytic coupling model of uniform reservoir seepage, wellbore pipe flow and screen pipe flow control, and then solved by finite difference. In 2015, volume 37, 4 th, journal of oil university in southwest (natural science edition), le ping and the like, aiming at a multilateral well of a bottom water reservoir, a reservoir subdivision method is used for replacing a shaft subdivision processing method, and a coupling model of reservoir seepage and shaft flow of a fishbone branch horizontal well of the bottom water reservoir is established. In volume 2 of 29 th of 2017, an integrated coupling model is established by a combined oil reservoir, ICD and shaft flow model of Chinese offshore oil and gas, Anyong and the like, and dynamic simulation of bottom water oil reservoir horizontal well ICD completion is solved and predicted by using a finite difference method.
There are major problems: (1) the shaft reservoir coupling model only contains the multiphase flow of the shaft, and usually adopts an analytic coupling method, neglects the influence of a well completion mode and the heterogeneity of a near-well reservoir, and can not consider the influence of an intelligent well completion tool on the flow direction and the flow, so the shaft reservoir coupling model can not be suitable for the coupling of the intelligent well completion and the reservoir model. (2) The well bore oil reservoir coupling model is usually based on the assumption of well bore steady-state flow, however, the intelligent well completion flow control device realizes the dynamic adjustment in the well, so that the well bore inflow rule changes in real time, therefore, the unsteady-state flow needs to be considered, and the coupling method of the intelligent well completion and the oil reservoir model is established.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an intelligent well completion and oil reservoir unsteady coupling method based on an iterative algorithm, the method is based on an independently operated oil reservoir simulator and a well completion simulator, the unsteady simulation of coupled flow is realized in an iterative mode, the mutual influence between the intelligent well completion and the oil reservoir and the unsteady flow characteristic generated by the intelligent well completion regulation and control are fully considered, the iteration is carried out at each time step, the iteration result is stepped according to time, the unsteady coupled flow simulation in a development period is realized, the development dynamics of the oil reservoir under the intelligent well completion dynamic regulation and control are accurately simulated and predicted, and a basis is provided for further optimizing the regulation and control and improving the development efficiency.
The purpose of the invention is realized by the following technical scheme.
An intelligent well completion and reservoir unsteady-state coupling method based on an iterative algorithm comprises the following steps:
step one, acquiring parameters required by coupling simulation: the method comprises the steps of oil reservoir size, pressure, porosity, permeability, saturation, bottom hole flowing pressure and intelligent well completion opening, wherein an oil reservoir simulator and a well completion simulator which run independently are utilized in the coupling process, and the results are coupled through the following algorithm steps;
step two, before coupling iteration, determining an initial state, wherein in an initial time step T ═ delta T, the pressure of the initial shaft pressure along the horizontal shaft is the bottom hole flowing pressure pBH(ii) a The oil reservoir model is an initial oil reservoir model, and at any other time step, the initial intelligent well completion and the pressure and the saturation of the oil reservoir are the calculation results of the previous time step;
step three, performing coupling iterative operation at any time step T-T, firstly, inputting a wellbore pressure curve into an oil reservoir simulator, and obtaining pressure and flow distribution of a well-containing grid through simulation calculation; secondly, inputting the pressure and flow distribution of the well containing grids into a well completion simulator, and obtaining the pressure distribution of the shaft and the pressure and flow of a new well containing grid through simulation calculation;
step four, when the time step T is compared with T, the oil reservoir simulator and the well completion simulator respectively calculate the pressure and the flow of the grid containing the well, if the error is smaller than the set error, the convergence condition is met, and the wellbore pressure/flow and the oil reservoir dynamic of the time step are obtained; if the error is larger than the set error, namely the convergence condition is not satisfied, repeating the third step, performing the next iteration step operation until the convergence condition is satisfied, completing the iteration of the time step, and determining the basis as follows:
in the formula: n is the number of well bore grids, qB,iCalculating the flow rate of the ith shaft grid in the oil reservoir simulation; q. q.sW,iCalculating the flow rate for the ith wellbore grid in a well completion simulation; q. q.soIs a reference flow rate; taking the average flow of the wellbore grids in the previous step; e is an error tolerance value, and 1% is recommended;
step five, storing the calculation result of the previous moment, repeating the step two, the step three and the step four, and performing iterative coupling of the next time step T which is T + delta T until all time steps of the set calculation time are performed;
and step six, outputting the calculation result of the iterative coupling, wherein the calculation result comprises the change of the oil deposit pressure distribution and the total output of the intelligent well completion shaft along with the time.
Compared with the prior art, the invention has the following advantages:
(1) the coupling method utilizes the concept of decoupling and recoupling, fully considers the influence of intelligent well completion on wellbore flow and heterogeneity of a near-well reservoir on the basis of independent operations of reservoir simulation and well completion simulation, so that the finally coupled model can fully consider the dynamic regulation and control process of the intelligent well completion, and is more suitable for coupling of the intelligent well completion and the reservoir model. (2) The iterative coupling method is suitable for each time step of the solving process, the results of the oil reservoir model and the intelligent well completion model are converged through the iterative coupling of each time step, the mutual influence and restriction of oil reservoir dynamic and intelligent well completion dynamic regulation and control are realized along with the advance of the time step, and the unsteady coupling of the oil reservoir and the intelligent well completion coupling model is finally realized.
Drawings
Fig. 1 is a schematic diagram of an unsteady coupling iteration flow.
FIG. 2 is a wellbore pressure profile for different iterations at an initial time step.
FIG. 3 is a wellbore inflow profile for different iterations at an initial time step.
Fig. 4 is a graph of the relative error for different iterations at an initial time step.
FIG. 5 is a result of a wellbore pressure profile after iterative coupling at different time steps.
FIG. 6 is a wellbore inflow distribution result after iterative coupling at different time steps.
Figure 7 is the total wellbore performance at different time steps.
Detailed Description
The invention is described in detail below with reference to an example of intelligent well completion and reservoir coupling.
An intelligent well completion and oil reservoir unsteady state coupling method based on an iterative algorithm comprises the following steps:
the method comprises the steps of firstly, obtaining main parameters required by coupling simulation, wherein the length, the width and the height of an oil reservoir are 1000m 500m 200m respectively, the initial pressure of the oil reservoir is 300MPa, the porosity is 20%, the permeability is 100mD, the oil saturation is 80%, the bound water saturation is 20%, the bottom hole flowing pressure is 250MPa, the intelligent well completion of the horizontal well is divided into 20 grids, and the opening degrees are all 1.
And step two, before coupling iteration, determining an initial state. At an initial time step (T ═ Δ T), the initial wellbore pressure along the horizontal wellbore is the bottom hole flow pressure (p)BH) (ii) a The reservoir model is an initial reservoir model, and at any other time step, the initial intelligent well completion and the pressure and saturation of the reservoir are the calculation results of the previous time step.
And step three, performing coupling iterative operation at any time step T-T. Firstly, inputting a shaft pressure curve into an oil reservoir simulator, and obtaining the pressure and flow distribution of a well-containing grid through simulation calculation; and secondly, inputting the pressure and flow distribution of the well-containing grids into a well completion simulator, and obtaining the pressure distribution of the shaft and the pressure and flow of the new well-containing grids through simulation calculation. Taking the initial time step as an example, the wellbore pressure and the wellbore inflow calculation result of the wellbore pressure in each iteration step are respectively shown in the figure, where itte ═ 0 represents the initial state, itte ═ 1 represents the first iteration, and so on, and the iteration flowchart is shown in fig. 1.
And step four, when the time step T is compared with T, the oil reservoir simulator and the well completion simulator respectively calculate the pressure and the flow of the grid containing the well, if the error is smaller than the set error, the convergence condition is met, and the wellbore pressure/flow and the oil reservoir dynamic state of the time step are obtained. If the error is larger than the set error, namely the convergence condition is not met, repeating the step three, performing the next iteration step operation until the convergence condition is met, and finishing the iteration of the time step.
The judgment basis is as follows:
in the formula: n is the number of well bore grids, qB,iCalculating the flow rate of the ith shaft grid in the oil reservoir simulation; q. q.sW,iCalculating the flow rate for the ith wellbore grid in a well completion simulation; q. q.soIs a reference flow rate; taking the average flow of the wellbore grids in the previous step; the epsilon is an error tolerance value, 1 percent is recommended to be taken as a conventional precision requirement, and 0.5 percent is recommended to be taken as a special precision requirement. The wellbore pressure distribution for different iterations at the initial time step is shown in fig. 2, the wellbore inflow distribution for different iterations is shown in fig. 3, and the relative error for different iterations at the initial time step is shown in fig. 4.
And step five, storing the calculation result of the previous time, repeating the step two, the step three and the step four, and performing iterative coupling of the next time step T which is T + delta T until all time steps of the set calculation time are performed.
And step six, outputting a calculation result of iterative coupling, wherein the calculation result mainly comprises the change of the oil deposit pressure distribution and the total output of the intelligent well completion shaft along with the time. In this example of the calculation, the wellbore pressure at different time steps is plotted in FIG. 5, the wellbore inflow at different time steps is plotted in FIG. 6, and the total production for the well over time is plotted in FIG. 7.
Principle description of the calculation method:
in the development process, the biggest difference of the intelligent well completion is that the inflow rate of a shaft/well completion can be dynamically adjusted in real time through a flow control device, so that the oil deposit and production dynamics are changed, and the aim of optimizing production is achieved. This means that intelligent completions interact with reservoir flow, and the coupling of flow changes over time, requiring the establishment of unsteady flow coupling methods. The unstable coupling problem of the model is mainly characterized in that different model solving methods are different, such as the coupling between a shaft and an oil reservoir model. The mathematical nature of this scientific problem is the coupling between linear systems (reservoirs) and nonlinear systems (wellbores). The existing shaft and oil reservoir coupling method is based on the assumption of a steady-state well model, so that only a steady-state result can be obtained, and the dynamic change of intelligent well completion and the coupling result of the well completion and the unsteady-state flow of the oil reservoir cannot be reflected. The intelligent well completion real-time analysis and optimization regulation and control are realized by accurately describing a main method of a linkage process of a well completion mode and reservoir dynamics by relying on unsteady coupled flow simulation of the well completion and the reservoir, so that the intelligent well completion method is accurately adjusted and efficiently utilized. Compared with the steady-state flow coupling, the unsteady-state coupling flow model needs to consider the linkage between the well completion and the oil reservoir and also needs to ensure the convergence and the operational efficiency of the unsteady-state model coupling result. The unsteady coupling flow model based on the iterative coupling method is provided, the problem that the well completion model is invariable in steady state in the coupling process is solved, the mutual influence of intelligent well completion and oil deposit in the dynamic production process is reflected, and the method is more reasonable compared with the existing method.
While the invention has been described in further detail with reference to specific preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (2)
1. An intelligent well completion and reservoir unsteady-state coupling method based on an iterative algorithm is characterized by comprising the following steps of:
step one, acquiring parameters required by coupling simulation: the method comprises the steps of oil reservoir size, pressure, porosity, permeability, saturation, bottom hole flowing pressure and intelligent well completion opening, wherein an oil reservoir simulator and a well completion simulator which run independently are utilized in the coupling process, and the results are coupled through the following algorithm steps;
step two, before coupling iteration, determining an initial state, wherein in an initial time step T ═ delta T, the pressure of the initial shaft pressure along the horizontal shaft is the bottom hole flowing pressure pBH(ii) a The oil reservoir model is an initial oil reservoir model, and at any other time step, the initial intelligent well completion and the pressure and the saturation of the oil reservoir are the calculation results of the previous time step;
step three, performing coupling iterative operation at any time step T-T, firstly, inputting a wellbore pressure curve into an oil reservoir simulator, and obtaining pressure and flow distribution of a well-containing grid through simulation calculation; secondly, inputting the pressure and flow distribution of the well-containing grid obtained by calculation into a well completion simulator, and obtaining the pressure distribution of the shaft and the pressure and flow of a new well-containing grid through simulation calculation;
step four, when the time step T is compared with T, the oil reservoir simulator and the well completion simulator respectively calculate the pressure and the flow of the grid containing the well, if the error is smaller than the set error, the convergence condition is met, and the wellbore pressure/flow and the oil reservoir dynamic of the time step are obtained; if the error is larger than the set error, namely the convergence condition is not met, repeating the step three, performing the next iteration step operation until the convergence condition is met, and finishing the iteration of the time step;
step five, storing the calculation result of the previous moment, repeating the step two, the step three and the step four, and performing iterative coupling of the next time step T which is T + delta T until all time steps of the set calculation time are performed;
and step six, outputting the calculation result of the iterative coupling, wherein the calculation result comprises the change of the oil deposit pressure distribution and the total output of the intelligent well completion shaft along with the time.
2. The iterative algorithm-based intelligent well completion and reservoir unsteady state coupling method according to claim 1, wherein the judgment basis that the convergence condition is satisfied in the fourth step is as follows:
where N is the number of well bore grids, qB，iCalculating the flow rate of the ith shaft grid in the oil reservoir simulation; q. q.sW，iCalculating the flow rate for the ith wellbore grid in a well completion simulation; q. q.soIs a reference flow rate; taking the average flow of the wellbore grids in the previous step; e is an error tolerance value, and 1% is taken.
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