"基于MATLAB的测量平差程序设计实践报告"

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基于MATLAB测量平差程序设计-创新实践报告.doc 在测绘工程学院的实践项目中，我们进行了基于MATLAB的测量平差程序设计实践。本报告将介绍实践的背景、意义、目的和方法，并总结实践过程中的收获和经验。一、创新实践研究的背景及意义在现代测绘工程中，测量平差是一项重要的技术。传统的测量平差方法工作量大，耗时长，并且容易出现人为误差。因此，开发一套自动化的测量平差程序是非常必要的。 MATLAB软件是一种功能强大的数学计算工具，可以进行复杂的数据处理和分析。通过利用MATLAB的优势，开发测量平差程序可以提高测量工作的效率和精度，减少人为误差。本次实践旨在通过设计和实现基于MATLAB的测量平差程序，来验证其在测量工程中的应用价值。通过这个实践项目，我们可以深入了解和掌握MATLAB的使用方法，并应用于实际的测量平差过程中。二、实践目的和方法本次实践的主要目的是设计和实现基于MATLAB的测量平差程序。具体目标包括： 1. 学习和掌握MATLAB软件的基本操作和编程方法； 2. 研究测量平差的基本原理和方法； 3. 设计和实现一个完整的测量平差程序； 4. 进行实际数据的测试和验证。实践过程主要分为以下几个步骤： 1. 学习MATLAB软件的基本操作和编程方法。通过阅读相关资料和参考代码，掌握MATLAB的基本语法和编程思想。 2. 研究测量平差的基本原理和方法。通过学习相关课程和参考文献，了解测量平差的基本原理和各种方法。 3. 设计和实现测量平差程序。根据测量平差的原理和方法，设计和编写MATLAB程序，实现测量数据的处理和平差计算。 4. 进行实际数据的测试和验证。收集实际的测量数据，使用编写的程序进行处理和平差，比较结果与传统方法的差异。三、实践过程中的收获和经验在本次实践中，我们深入学习和掌握了MATLAB软件的使用方法，并成功设计和实现了一个基于MATLAB的测量平差程序。通过与传统方法的对比，我们发现MATLAB程序在效率和精度上都有明显的优势。此外，通过实践项目，我们还锻炼了自己的团队合作能力和问题解决能力。在程序设计和调试过程中，我们积极合作，互相帮助，共同解决了很多困难和问题。总之，本次基于MATLAB的测量平差程序设计实践使我们对MATLAB软件有了更深入的了解，并掌握了一种新的测量平差方法。同时，实践还提高了我们的团队合作能力和解决问题的能力，为今后的工作和学习打下了坚实的基础。希望今后能在实际的工作中应用所学知识，进一步提高测量工作的效率和精度。

of the simulation,time-step limitations not as severe, thanks to a better stability of the

geometrical grid, numericaldiffusion easier to control, faster solutions with respect to FD

approach [7].

Streamline simulation solves mono-dimensional equations along streamlines, which

means itsolves multiple streamlines in parallel, and the fluid transport, which for FD approach

occursbetween grid cells [8], occurs along streamlines: this gives an immediate answer in terms

of howthe streamlines (connecting injector and producer wells—to say, well pairs) are

distributed, sothat the fluid trajectories and their rates at the wells are known at every time step

[9, 10]. Thanksto the available information, the distribution of injected water volumes can be

modified, and amore effective production strategy can be planned to maximize oil recovery [4,

5]. Same as for FDsimulation, when using streamline-based simulation a good balance must be

maintained betweenpressure updates and computational speed.

In conclusion, 3DSL is simpler from a reservoir description (includes both flow physics

andpetro-physical/geological information) point of view, but it cannot take into account

importantparameters such as capillary pressure or cross flow effects: moreover, fluid

compressibility cannot beeasily taken into account and mass conservation errors may occur

while mapping between pressuregrid and streamline grid therefore being incomplete or

inadequate for most of real reservoirs. Everyfurther detail can be found in previous works [11].

THE IDEA OF THE STUDY

In this paper the use of the two software as complementary tools to optimize water

injectionis shown: ECLIPSE appeared to be the most reliable software to simulate reservoir

models andto be essential to verify 3DSL’s results accuracy upstream and downstream of the

methodologydeveloped for this research, while 3DSL has been shown to be simpler and

usually faster, providinginformation not obtainable by means of FD simulation, and which

could be exploited to save atrial-and-error process trying to find the best injection scheme

The idea of the study resulted from previous works [12].

The research presented here follows a three step workflow. In the first part of the study,

thetwo software were used to simulate simple synthetic models. Once the results obtained from

thetwo simulators were checked for consistency, a method was developed to exploit 3DSL’s

features.The most interesting pieces of information available from 3DSL are the connections

between wellpairs (injector-producer) and the rates at the wells and the connections. With this

informatioavailable, the streamline simulator, coupled with Matlab, was involved into an

automatic procedurethat reallocated water injected volumes in order to optimize water injection

campaigns, for all the examined cases.This was gained by a customized Matlab script, written

for this specific purpose, which automaticallyinteracted with the streamline simulator,

processed the input data supplied by the softwaregiving back new input rates to be used for the

following simulation run. This was done for a fixednumber of runs to iteratively converge to

the optimal injection scheme. From preliminary analyses[13] the procedure was shown to be

effective

Eventually, in the third experimental phase, the last rates calculated from Matlab were

inputinto ECLIPSE to check the procedure’s effectiveness and the added value in terms of oil

recoveryimprovement.

THE AUTOMATED PROCEDURE AND THE EXPERIMENTAL METHOD

Let’s now focus on the second and main part of the experimental study which, as we said,

consistedin writing a customized Matlab code. Streamline answers in terms of streamlines

distribution(connecting injector and producer wells—or well pairs) and fluid rates at the wells

are writtenat every time step in a 3DSL output file named ∗.WAF, which is crucial for the

entire codingdescribed below and for its application.

The endeavor of the Matlab customized code is the optimization of the displacement

processthrough a gradual reallocation of injected water rates, carried out by increasing the

water volumeinjected at the highly efficient connections and decreasing it at the poorly

efficient connections.The term injection efficiency (for a well pair connection) stands for the

ratio between offset oilproduced thanks to water displacement and the amount of water injected

at a certain well. In ananalogue manner the injection efficiency of an injector well is the ratio

between offset oil at allproducers connected to it and the total water injected at the same well.

The approach used forthe current application was aimed at increasing the oil production

through a better use of a givenwater volume available for injection. The Matlab code written

for this purpose mainly works asfollows: the data needed for the procedure are read from

3DSL’s output ∗.WAF file and loadedinto Matlab environment, then they are processed

throughout the code; eventually new rates forthe next simulation run are output to 3DSL. This

is all done automatically with an approach aimedat maximizing oil production through a more

efficient use of a given water volume available forthe injection [13]. The code steps are here

summarized [12]:

1st step consists in: establishing the number of iterations to be run for the procedure of

ratereallocation and their duration; fixing the volume of water to be injected and the target

liquid rate.

2nd step consists in: running a ‘do nothing’ 3DSL simulation in order to choose a

properstarting time for the entire procedure of rate reallocation (usually the reallocation starts

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