Development of water flood model for oil production enhancement

Jetina J. Tsvaki, Dmitry O. Tailakov, Evgeniy N. Pavlovskiy

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

Аннотация

Main goal of any industry is to increase productivity which in oil and gas field is to increase reservoir oil asset by producing oil in an effective and economically efficient manner. The objective of the study is to develop a water flood model for oil production enhancement using artificial neural networks and provide a model that maximizes oil production for a given water injection that in turn will extend mature fields life and decrease operational costs. Using the data comprising of daily water injection rates, oil production rates, water production, and gas production from the year 2004 to 2016 for 577 injection wells, 1344 production wells, and 36 events which had occurred during the course. Comparative analysis on the deep neural models such as Multi-Layer Perception, Convolutional Neural Networks, Long Short-Term Memory, and Gated Recurrent Neural Networks are used, and Gated Recurrent Neural Networks outperformed them. To minimize the loss and improve the performance of the water flood model tabular data mix-up was adopted on all the models above. The results showed that the data mixed up Gated Recurrent Neural Network outperformed all the other models. To maximize the oil production Nelder-Mead optimization method was adopted to find appropriate water injection rates. A simple two-layered multi-layer perceptron was used in modeling the nonlinear relationship between water injection and oil production to avoid function complexity.

Язык оригиналаанглийский
Название основной публикацииProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
ИздательInstitute of Electrical and Electronics Engineers Inc.
Страницы46-49
Число страниц4
ISBN (электронное издание)9780738131115
DOI
СостояниеОпубликовано - 14 ноя 2020
Событие2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020 - Virtual, Novosibirsk, Российская Федерация
Продолжительность: 14 ноя 202015 ноя 2020

Серия публикаций

НазваниеProceedings - 2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020

Конференция

Конференция2020 Science and Artificial Intelligence Conference, S.A.I.ence 2020
СтранаРоссийская Федерация
ГородVirtual, Novosibirsk
Период14.11.202015.11.2020

Fingerprint

Подробные сведения о темах исследования «Development of water flood model for oil production enhancement». Вместе они формируют уникальный семантический отпечаток (fingerprint).

Цитировать