In sectors such as energy, aquaculture and chemical processing, the effectiveness of both mechanistic and data-driven models is frequently limited by poor-quality training data, which obscures underlying physical laws and degrades predictive accuracy. Such quality issues commonly manifest as anomalies in the dataset, ranging from sensor faults to process upsets and incorrect operator inputs. While traditional rule-based filtering can remove a subset of the anomalies, it is unable to leverage contextual information stored as unstructured text, such as engineer comments and operational logs that explain under what conditions the data was collected. We propose a text-informed data filtering method that leverages large language models (LLMs) to integrate unstructured operational data with tabular sensor data. The method consists of two steps: (1) contextual anomaly detection by reasoning over both data modalities, and (2) generation of a binary mask that is applied to filter the training dataset for model development. To investigate the potential benefit, a steady-state soft-sensor regression model, namely a virtual flow meter that estimates well flow rates, is used as the downstream evaluation target. Experiments on a synthetic oil and gas dataset show improved model performance compared to traditional rule-based filtering. Ablation studies show that textual operator comments can provide filtering-relevant signals beyond sensor data alone, with the multimodal approach reducing downstream MAPE from 7.1% to 5.4% relative to sensor-only filtering. This work demonstrates that integrating unstructured operational knowledge with sensor data through LLMs can improve data quality for industrial machine learning applications, offering a scalable alternative to manual data curation.
Figures & tables
Figure 1: Traditional flow of information in industrial data curation. The operator synthesises background domain knowledge, operational context, and sensor readings into unstructured comments. These comments, together with the raw sensor data, then inform a manual curation step that determines which data points enter model training.
Category
Perturbation type
Physical mechanism
Scaling
Well parameter ( fD , D )
Mineral deposits restricting flow
Hydrate formation
Well parameter ( fD , D )
Ice-like plugs in the wellbore
Liquid loading
Well parameter ( α )
Inability to lift liquids
Emulsion
Well parameter ( fD )
Oil-water emulsions increasing friction
Wax deposition
Well parameter ( fD )
Wax build-up increasing friction
Data quality
Simulated parameter
Sensor faults and communication failures
Table 1: Anomaly groups.
Method
RQ1
RQ2
RQ3
Uniform (no filter)
—
—
—
Tukey
B
Mahalanobis
B
LLM (multimodal)
∙
∙
B
LLM (text only)
abl.
LLM (sensor only)
B
Table 2: Role of each method per research question. B = baseline, ∙ = tested, abl. = ablation, — = reference bound.
Figure 3: Example of a dataset for a single well and the model fitted to the nominal data points.
Method
MSE ((Sm 3 /h) 2 )
MAPE (%)
W10 (%)
Uniform (no filter)
148 [123, 176]
12.5 [11.5, 13.6]
51 [46, 55]
Tukey
112 [86, 144]
10.0 [8.9, 11.2]
66 [62, 70]
Mahalanobis
123 [98, 151]
9.4 [8.3, 10.6]
66 [62, 71]
LLM (multimodal)
53 [44, 64]
5.4 [4.9, 5.9]
87 [85, 89]
LLM (text only)
86 [71, 103]
8.0 [7.4, 8.7]
78 [76, 81]
LLM (sensor only)
88 [74, 103]
7.1 [6.5, 7.8]
84 [82, 86]
Table 3: MSE, MAPE and W10, pooled across wells and classification runs (95 % confidence intervals).
Figure 4: Results presented as a cumulative performance plot.
Method
Match (%)
LLM (multimodal)
96.6
LLM (text only)
100.0
LLM (sensor only)
92.7
LLM + Tukey
96.4
LLM + Mahalanobis
97.6
Table 4: Run-to-run consistency: Match(a,a) across all training wells (percent matching decisions).
Figure 5: Data points of the well with index 745 processed by the LLM (multimodal) method. Data points of particular interest are numbered.
1The Hong Kong University of Science and Technology (Guangzhou) · 2The Hong Kong University of Science and Technology · 3Shanghai University of Finance and Economics +2