# Hybrid and Automated Machine Learning Approaches for Oil Fields Development: the Case Study of Volve Field, North Sea

arXiv: https://arxiv.org/abs/2103.02598 · HTML: https://mlsanity.com/paper/2103.02598/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 57 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Deep-Learning Inversion of Seismic Data](https://mlsanity.com/paper/1901.07733/) (2019)
- [REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model](https://mlsanity.com/paper/1906.08587/) (2019)
- [Using Machine Learning for Model Physics: an Overview](https://mlsanity.com/paper/2002.00416/) (2020)

## Built on by (most cited first, 1 of 1)

- [TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data](https://mlsanity.com/paper/2605.00060/) (2026)

## Abstract (arXiv)

The paper describes the usage of intelligent approaches for field development tasks that may assist a decision-making process. We focused on the problem of wells location optimization and two tasks within it: improving the quality of oil production estimation and estimation of reservoir characteristics for appropriate wells allocation and parametrization, using machine learning methods. For oil production estimation, we implemented and investigated the quality of forecasting models: physics-based, pure data-driven, and hybrid one. The CRMIP model was chosen as a physics-based approach. We compare it with the machine learning and hybrid methods in a frame of oil production forecasting task. In the investigation of reservoir characteristics for wells location choice, we automated the seismic analysis using evolutionary identification of convolutional neural network for the reservoir detection. The Volve oil field dataset was used as a case study to conduct the experiments. The implemented approaches can be used to analyze different oil fields or adapted to similar physics-related problems.