Core Analysis Services - Characterization | Schlumberger

Core Analysis Services

Quantified insight into porosity, permeability, and fluid saturation

Schlumberger lab worker looking at sample.

Routine core analysis

Measurement of basic properties helps you determine if a rock contains a fluid-filled space (porosity), identify the hydrocarbons in that space (saturation), and assess the ability of those hydrocarbon fluids to be produced (permeability).

Routine core analysis (RCAL) can also employ core gamma logging to definitively link the core depth to logging depth. Computed tomography (CT) scans are conducted to indicate core heterogeneity.

Special core analysis

Detailed understanding of a reservoir requires additional measurements obtained through special core analysis (SCAL). Examples of our capabilities include

  • calibrating electrical logging measurements of porosity and saturation
  • determining a formation-specific cutoff value for the relaxation time from a nuclear magnetic resonance (NMR) log
  • measuring capillary pressure to indicate distributions of pore throats and evaluating saturation distribution as a function of height in the formation
  • measuring the multiphase flow character of the formation
  • evaluating wettability.
Core samples

Formation damage testing

Return permeability after mud invasion, fluid-rock interactions, fluid-fluid interactions, and damage caused by pressure and temperature changes in the reservoir provide useful data for addressing formation damage to maximize productivity.

Infrared spectroscopy services

Our rapid infrared spectroscopy technique provides both mineralogy and total organic carbon (TOC) simultaneously on small samples of the formation. The method can also be used on cuttings from legacy wells to help you understand differences in productivity between wells.

TerraTek HRA heterogeneous rock analysis service

TerraTek HRA heterogeneous rock analysis service is a workflow for improving the representativeness of core sampling in heterogeneous reservoirs. It also facilitates the integration of core, log, and seismic data.

Rooted in a foundational quantitative, unbiased classification of log data to determine zones of consistent or differing log response. The classification then facilitates the selection of samples for core testing up front and the integration of both quantitative and qualitative data across multiple disciplines and multiple scales (such as core, log, and seismic surveys) when testing is complete.

Your result: more accurate identification of target zones and the prospect for spreading knowledge across future wells using log data alone. It can be possible to extend this knowledge to regions that lack log data through upscaling and integration with seismic data.

Modeling of integrated datasets

Share This