Accurate estimation of reservoir properties from seismic data remains challenging when well-log labels are sparse but three-dimensional seismic observations are abundant.
We present CoGameReg, a cooperative-game-based semi-supervised regression framework that uses contribution-weighted predictions of heterogeneous regressors to construct shared pseudo-labels.
This design makes each regressor's influence explicit in the learning targets generated for unlabeled seismic samples under sparse well control.
The Tri-Game Shapley mechanism combines a rank-MSE performance game with a pairwise consistency game to estimate nonnegative regressor weights from labeled training data.
These weights remain fixed during collaborative training and are reused for final prediction fusion.
Candidate pseudo-labels are screened using a common local k-nearest-neighbor error-reduction criterion before retraining.
We evaluate CoGameReg on 10 public regression datasets, a synthetic three-dimensional P-wave velocity prediction task, and a field water-saturation application.
The benchmark advantage is most pronounced at the 2.5% labeled-data ratio and decreases as supervision increases.
CoGameReg obtains the best blind-well metrics in the synthetic experiment.
In the field application, predictions clipped to 0-100% yield a pooled coefficient of determination of 0.5942, a root mean square error of 5.7441 percentage points, and a Pearson correlation of 0.8265 across three blind wells.
CoGameReg has the highest Pearson correlation among the four field methods.
The results support the use of CoGameReg to integrate sparse well labels with dense seismic attributes, with performance varying across metrics and well locations.