The Effective Diagnostic Capability of Pulsed Neutron Logging for CCS Monitoring Purposes

The actual importance of carbon capture and storage (CCS) projects requires in-depth studies on several disciplines. In particular, measurement, monitoring, and verification (MMV) plans include critical activities at the so-called spy wells for the proper understanding of carbon dioxide (CO2) plume development far from the injectors. In this respect, time-lapse pulsed-neutron logging (PNL) represents a mainstay for the quantitative evaluation of fluid saturation changes behind casing. However, the latter task may not be straightforward in case CO2 injection is performed into depleted gas reservoirs. This paper deals with a deep study to evaluate the diagnostic capability of various PNL measurements for fluid identification and saturation monitoring purposes in CCS projects. First, accurate analytical and numerical modeling of typical PNL responses of mixtures of water, reservoir gas (methane or others), and CO2 has been performed. These include fast neutron interactions, inelastic/elastic scatterings, and capture, together with their dependence on pressure, temperature, and acquisition environment. The outcomes of the first step lay the groundwork for the definition of the most effective PNL interpretation approach, as appropriate. In detail, after the selection of fit-for-purpose curves and their physics-based models, a joint inversion is performed to reconcile models and actual measurements in order to solve for water saturation, reservoir gas, and CO2 fractions in selected cases. The uncertainty of the outputs is also quantified by means of an ad-hoc Monte Carlo approach, starting from the standard uncertainties of the input PNL data. In turn, two real case studies are presented. The first is a depleted gas reservoir characterized by multiple layers hydraulically separated and with aquifers of different strengths. The second is more homogeneous from the lithological standpoint, but it is highly depleted since no strong aquifer has provided pressure support during the development phase. For both, baseline PNL acquisitions have been performed in spy wells to fix the water saturation scenarios before CO2 injection and to calibrate the PNL model parameters. Then, several simulations of PNL response have been performed in order to forecast the deviation from the acquired baselines, according to the possible arrival of plumes composed by reservoir gas-CO2 mixtures with different relative concentrations, in case displacing different amounts of water volume fractions and at different pressure and temperature regimes. The driver for the selection of the above scenarios is the dynamic behavior during injection obtained from the available dynamic reservoir models. Therefore, random errors have been generated for the simulated PNL curves to be used for the subsequent uncertainty quantification in obtaining the desired water saturation and reservoir gas-CO2 relative concentrations, mimicking future time-lapse interpretations. The latter represents a useful template to understand the real PNL monitoring capability in such environments and the best subset of neutron interactions to exploit for the purpose. The presented workflow provides robust insights on when and how much PNL monitoring is effective in a given CCS project. This information is fundamental for the MMV plan to schedule the proper time-lapse PNL campaign.