2 research outputs found
Developing a Hybrid Data-Driven, Mechanistic Virtual Flow Meter - a Case Study
Virtual flow meters, mathematical models predicting production flow rates in petroleum assets, are useful aids in production monitoring and optimization. Mechanistic models based on first-principles are most common, however, data-driven models exploiting patterns in measurements are gaining popularity. This research investigates a hybrid modeling approach, utilizing techniques from both the aforementioned areas of expertise, to model a well production choke. The choke is represented with a simplified set of first-principle equations and a neural network to estimate the valve flow coefficient. Historical production data from the petroleum platform Edvard Grieg is used for model validation. Additionally, a mechanistic and a data-driven model are constructed for comparison of performance. A practical framework for development of models with varying degree of hybridity and stochastic optimization of its parameters is established. Results of the hybrid model performance are promising albeit with considerable room for improvements
Modelling oil and gas flow rate through chokes: A critical review of extant models
Oil and gas metering is primarily used as the basis for evaluating the economic viability of oil wells. Owing to the
economic implications of oil and gas metering, the subject of oil and gas flow rate measurement has witnessed a
sustained interest by the oil and gas community and the academia. To the best of the authors’ knowledge, despite
the growing number of published articles on this subject, there is yet no comprehensive critical review on it. The
objective of this paper is to provide a broad overview of models and modelling techniques applied to the estimation
of oil and gas flow rate through chokes while also critically evaluating them. For the sake of simplicity
and ease of reference, the outcomes of the review are presented in tables in an integrated and concise manner.
The articles for this review were extracted from many subject areas. For the theoretical pieces related to oil and
gas flow rate in general, the authors relied heavily upon several key drilling fluid texts. For operational and field
studies, the authors relied on conference proceedings from the society of petroleum engineers. These sources
were supplemented with articles in peer reviewed journals in order to contextualize the subject in terms of
current practices. This review is interspersed with critiques of the models while the areas requiring improvement
were also outlined. Findings from the bibliometric analysis indicate that there is no universal model for all flow
situations despite the huge efforts in this direction. Furthermore, a broad survey of literature on recent flow
models reveals that researchers are gravitating towards the field of artificial intelligence due to the tremendous
promises it offers. This review constitutes the first critical compilation on a broad range of models applied to
predicting oil and gas flow rates through chokes