"Consistency of interpretation" is the hallmark of quality work for the IHS Markit sub-surface consulting team. Each of our disciplines (Geology, Geophysics, Petrophysics, Reservoir Engineering and Reservoir Simulation, Reserves and Economic Evaluation, Well Testing, Wellbore Modeling, Pipeline Modeling, Development Planning and Uncertainty [Monte Carlo] Assessment) works from available data to provide one or more of:

• Estimates of hydrocarbons-in-place, recovery factors and recoverable volumes
• Forecasts of production rates and ultimate recovery, which become inputs for economic value assessments, exploration and development plans and the de-bottlenecking and optimal operation of producing fields / entities

Since it is not possible to directly measure the hydrocarbons that exist in the ground or the recovery factor, we can never know for certain that estimates are correct. However, we can achieve a sufficiently high degree of reliability for investment and reporting purposes by ensuring that each discipline's analysis is consistent with the dataset that it has and on integrated projects, that each discipline's interpretation(s) are consistent with the other disciplines.

Project scope for the team can be as small as a single well test analysis or wellbore model, or can be as large as a pool, field or offshore concession for well / facility optimization studies, exploration assessments and greenfield / brownfield developments. The largest project scope may be an estimate of company value for Reserves and Economic Evaluation projects.

Data gathering and rigorous quality control to ensure the correctness of the data is the starting point for all disciplines, irrespective of the size and scope of the project. Given that onshore well spacing densities for conventional reservoirs seldom exceed 1 well per ¼ mile and are often much less during exploration and development and for offshore projects, it is crucial that the limited amount of data that is available is accurate. Something as simple as an off-depth perforation interval or an incorrectly reported pipeline operating pressure can completely invalidate an analysis, particularly if it is the only data point available at the time of the study. At the very least, it will take additional time and effort to identify and correct the erroneous data.

Integrated exploration assessments, greenfield/brownfield development studies and large reserve evaluations can involve all the technical skill sets within the sub-surface consulting team. Each discipline must first develop an understanding of its own dataset and the conclusions that can be drawn from it (e.g. geology from regional studies, core and log data, geophysics from seismic data, petrophysics from core and log data, reservoir engineering from production data, etc.). To reduce bias in the interpretation, there should be only limited interaction between the disciplines until each has become thoroughly familiar with the strengths and uncertainties that are inherent to its dataset.

Discussion and integration between the earth science disciplines then follows, to develop a consistent interpretation of the prospect/area geology, including the depositional environment, elevation and sea level changes and the impact on exposure and burial, groundwater flow and post-depositional tectonic activity. It is at this stage that each earth scientist shares their piece(s) of the puzzle to develop a 3-D interpretation of the prospect's geometry and the fluid distribution within.

Volumetric in-place and recoverable hydrocarbon estimates are developed from the geologic/geometric model with appropriate ranges of uncertainty for the input parameters. In brownfield developments, production and pressure data can offer a second, independent method of estimating in-place and recoverable hydrocarbons using material balance techniques. Consistency between the two methods improves the confidence in and reliability of the estimates.

Extract the most value from your reservoir; request more detail on our Reservoir Engineering Consulting services.

 
Posted 13 July 2017

This article was published by S&P Global Commodity Insights and not by S&P Global Ratings, which is a separately managed division of S&P Global.