Abstract
In the middle and late stages of heavy oil development, formulating a scientific and reasonable mining plan is the key to improving oilfield efficiency. At present, steam stimulation is still the main development method of heavy oil. The determination of its production is not only limited by boiler conditions, surface pipelines, and wellbore conditions but also by the steam absorption capacity of the formation. Therefore, local analysis cannot achieve the best effect in the whole process of steam stimulation. The mechanism model is the most commonly used method to predict heavy oil production, but too many idealized assumptions make the prediction results quite different from the actual production situation. With the rapid development of machine learning, people can achieve rapid prediction of production through field data. However, when the range of the actual parameter is small, the generalization ability of the model is weak and overfitting occurs. Based on the above background, this paper conducts a coupling study on surface steam pipeline flow, steam injection wellbore flow, and formation flow from the perspective of data-driven. Firstly, based on the correlation coefficient and the feature selection of Random Forest, the importance of the characteristics affecting liquid production and water content was ranked. Secondly, through the comparison of five typical machine learning algorithms, we select the optimal prediction model and optimal characteristics suitable for the sample of this paper. Finally, because of the poor generalization ability of the prediction model, we sampled the mechanism model and increased the diversity of steam dryness samples. We find that the accuracy of the optimal prediction model is improved and the generalization ability of the model is improved after the training of new samples. This paper provides a new idea for the production prediction of heavy oil steam stimulation reservoirs, which is helpful for the efficient development of heavy oil reservoirs.
Highlights
As a rich mineral resource, heavy oil has important practical significance for its efficiency and economic development
Hou and Chen proposed an improved steam stimulation productivity prediction model based on previous studies and introduced the shape coefficient to correct the influence of the overlap phenomenon in the steam injection process [5]
Zheng et al established a new analytical model for steam stimulation productivity prediction based on the Marx–Langenheim model [6]. e model shows an exponential change in the temperature field in the hot oil area, which is more in line with the actual reservoir
Summary
As a rich mineral resource, heavy oil has important practical significance for its efficiency and economic development. For a given heavy oil block, the mining effect of steam stimulation depends on the injection and production parameters and the degree of thermal energy utilization of the injected steam. E dynamic prediction of steam stimulation wells is the basis of injection parameter design and production design optimization. The mechanism model developed more and more perfectly but compared with the reservoir numerical simulation method, the parameters considered are much less. In 1974, Coats et al developed a threedimensional three-phase steam injection thermal oil recovery model [10] On this basis, several reservoir numerical simulation software such as CMG series and Eclipse series have been developed. We sample different underground conditions by reservoir numerical simulation and describe the reservoir mining state by partial differential equations, but its accuracy is based on accurate geological models. E content of this paper is arranged as follows. e second part introduces the data source and data preprocessing. e third part is the establishment and verification of the input and output model of the reservoir system based on data-driven. e fourth part is the establishment and verification of the input and output model of the oil reservoir system based on hybrid data-driven. e fifth part is the conclusion
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