Fluid characterisation is critically important to effective reservoir management. Misinterpreting reservoir properties, for instance, can result in non-optimal well placement, completion strategy, and facilities design as well as large errors in reserves, drainage volume and production rate predictions. Characterisation of reservoir fluids based on a single technique or technology such as mud-gas logging, wireline logging, PVT, or wellsite chemistry is standard in most E&P operations. Integrated approaches involving multiple tools and technologies are still relatively new to the industry, but Shell, through its FEAST (Fluid Evaluation and Sampling Technology) global centre of expertise, is routinely implementing such integrated approaches for fluid characterisation. For example, advanced mud-gas logging data can be used for fluid facies prediction and picking sampling points for formation testing; PVT data can be calibrated against mud logging data; and downhole fluid analysis can provide fluid property measurements real time, during sampling. Integration ensures that the evaluation objectives are addressed using the most optimal technologies and workflows, maximises the value of information, and improves operational decision-making.

The interpretative workflow also involves integrating advanced mud-gas logging technology data with formation testing, Geochemical, and PVT data to predict fluid properties in unsampled wells. Understanding reservoir fluid properties ahead of the actual sampling operation enables smart decisions while drilling and logging, which can save millions of dollars in operational costs. The sampling process can be optimized in terms of where and when to sample, and how many samples to collect; and the quality of the fluids collected can be substantially improved. Because each technique has its strengths and limitations, integration is far more valuable than relying on any single source for interpretation. Ultimately, no technique is used in isolation, and no interpretation is worked in a vacuum without adequately understanding the geological setting and the fluid sourcing, migration, and trapping mechanisms.


Fluid properties are, obviously, key parameters to understanding the economic viability of opportunities within the E&P industry. Their variability and distribution within a field will have enormous impacts on facility design, well count, and operation expense. Thus, a full understanding of their physical-chemical behavior is warranted prior to any final investment decision. In addition, multiple high quality fluid samples within a given reservoir unit yield insights into reservoir fluid grading and compartmentalization; complexities that have to be captured in static and dynamic models for determining in place volumes and ultimate recovery. Notionally, during the exploration and appraisal phases of a project, key sub-surface and surface uncertainties are translated into data acquisition requirements, and it is up to the team to select fit-for-purpose logging suites, rock, and fluid acquisition and analysis programs to address these uncertainties.

Accurate fluid models and integration of independent data sets become keys to addressing potential problems during the development and production life cycle of the field as well as reservoir management and surveillance programs. For examples, the reader is referred to Honarpour et al. (2006) and Nagarajan et al. (2007).

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