Application of deep learning methods to estimate multiphase flow rate in producing wells using surface measurements

In the past, the problem of predicting the multiphase flow rate in producing wells has been tackled using the Gilbert correlation. The Gilbert correlation offers a quick solution and requires quantities that are readily available at the surface, making it a staple among solutions in many publications. However, the correlation is limited in applicability to local data, and in some cases, multiple parameter settings are required when flow conditions change. To address these points, we introduced a systematic comparison of applying Gilbert correlation and Deep Learning (DL) algorithms to the problem of liquid flow prediction. We determined that some of the issues, specifically the need for multiple models, can be alleviated using DL methods. In this work, available wellhead measurements that can be collected at surface were used in modeling their relationship to well production rate. In addition, features were extracted automatically using autoencoders to generate additional inputs. The algorithms used for forecasting were designed using standard and customized DL architectures. The results of both DL and existing solutions from the literature were compared systematically to determine the usefulness of the DL methodology developed. The study developed a novel methodology for forecasting well liquid and multiphase restricted flow rate using wellhead surface measurements and demonstrated the application to real field data. The method was found to be an efficient utilization of data that are readily available and do not require well intervention to measure.

Automated summary

Comparing the Gilbert correlation with Deep Learning algorithms for liquid flow prediction, it was found that DL methods can alleviate some issues and provide a novel approach for forecasting well liquid and multiphase restricted flow rates.