Application of Self-Supervised Autonomous Agent Framework With Growing Scheme for Digital Transformation of Elder/Existed Well Potentials Discovery
Authors: Chen H. , Zhao Q. , Lu W. , Jin W. , Gu S. , Li Y. , Wang X. 
Published: February 2025

Abstract

Objectives/Scope
The manuscript proposes an agent framework for elder (existed) well re-assessment. Through the allied application with different generative artificial intelligence (AIGC) models, the self-supervised feedback capability of the framework with a growing scheme, continuously stimulates the reasoning abilities of large AIGC models is proposed. With less rely on fine-tuning, the approach proposed aims to realize the precision improvement of curve re-construction, strata prediction, and potential layer interpretation for existed tight gas wells, especially in terms of dig deeper into even detail scenes.

Methods, Procedures, Process
The manuscript proposes a upgrade version of self supervised agent framework for purpose of assisting existed well re-evaluation among northwestern oil&gas field of China. Based on explainable self-supervised agent group framework (ESSA) for putting-up academic mechanisms, business models and AI components altogether, an autonomous self-supervised agent framework (CISFA) along with generative AI large-model (AIGC-LLM) was introduced to construct copilot for re-evaluation activities. Upgraded from former contributions, a growing scheme with original designed attention spreading scheme is conducted in this manuscript, thereby growing "superior individuals" for in-depth analysis among specific tasks, namely, curve prediction, strata classification and potential useful layer recommendation.

Originality
The manuscript demonstrates the innovation of AI-native architecture with following originality: (1) The AI-native application jointly formed by the agent cluster and AIGC possesses the capability to address multi-scenarios cascading problems. (2) Through the multi-agent framework, a closed-loop agent cluster with growing scheme combined with attention spreading mechanism is constructed, which realizes adaptive-learning and improvement among collective intelligence through accurate model selection. This approach continuously optimizes the performance of re-evaluation of existed wells, significantly reducing the ongoing operational consumption of the model in practical applications.

Results
50+25 existed wells among 2 various blocks, all with 30+ years, located in two distinct blocks were taken into account. Utilizing the framework introduced in this manuscript, we evaluated the accuracy of model selection, the effectiveness of well logging curve reconstruction, the classification of strata (oil, water, gas, sandstone, mudstone, and oil-water coexistence), and the capability of recommending potential layers. The experimental results demonstrate that the proposed framework possesses feasible agent based "Tool-using" capabilities. Through self-supervised prompting engineering provided by the agent framework proposed in this manuscript, it can continuously unleash the potential of AIGC models, resulting in satisfactory interpretations of potential layers, with additional agent growing scheme performed further accurate outcomes in detailed existed-well assessment tasks.