The foundation behind reliable results

Quality inversion results depend on quality input. Our conditioning tools address noise, amplitude imbalances, and alignment issues to ensure your data are optimised for AVO inversion. These preprocessing steps reduce uncertainty and improve the reliability of everything that follows.

All algorithms are developed in-house with particular attention to amplitude and wavelet shape preservation throughout the conditioning workflow.

Rock physics analysis establishes the relationship between elastic properties and reservoir parameters such as porosity, lithology, and fluid content. We build locally calibrated models that capture the specific behaviour of your reservoir, forming the foundation for meaningful property prediction away from the wellbore.

Our rock physics model honours physical bounds (Voigt, Reuss, Hashin-Shtrikman) and supports multi-mineral fluid substitution, anisotropy, and pressure sensitivity.

Reliable well ties are essential for confident inversion results. Our wavelet estimation tools are designed for varying degrees of well control and data quality, allowing us to discard unnecessary assumptions and achieve unbiased calibration between domains.

Our methods support single and multi-well approaches, with options for spatially and temporally variant wavelets and anisotropy.

Seismic data lack the low-frequency content needed for absolute property estimation. We build geologically consistent low-frequency models using well logs, horizons, and velocity trends, providing a robust foundation for inversion and reducing uncertainty in the final property volumes.

Our algorithm handles faults, varying depositional systems, and facies-driven modelling, with built-in blind well testing to validate model accuracy.

Our simultaneous AVO inversion processes all input stacks together, solving for the best-fitting earth model across the full bandwidth. The resulting elastic property volumes form the basis for further interpretation, rock physics inversion, and lithology prediction.

Our globally optimised algorithm uses simulated annealing without linearising AVO functions or assuming relationships between terms, and handles large datasets without segmentation.

We offer multiple approaches to lithology and fluid prediction: Bayesian classification, rock physics-driven inversion, and direct probabilistic inversion. The choice depends on your data and objectives. Each approach delivers outputs that integrate directly into reservoir characterisation workflows.

Our classification uses non-Gaussian probability density functions estimated via kernel-density techniques, providing robust probability distributions rather than single-point estimates.