No abstract

Log While Drilling (LWD) Density-Neutron allow to calculate, in real/near real time, a continuous porosity and shale volume curves. Sometimes log acquisitions from different tool types are available. In these cases, attention should be paid to the reading characteristics of the individual tools, to calibrate the petrophysical interpretation in a consistent way. Generally, the LWD interpretation can be considered more critical than Wire Line Log WLL because the operation timing strongly limits an accurate QC of the data. Different Nuclear tools involve different measurement system for density and different energy source as well as different neutron energy range for neutron. During the field development phase, the petrophysiscal analysis can deal with various type of Density and Neutron log, even in distinct phases of the same well, making the lithology recognition, the shale volume and the effective porosity evaluation more difficult and even misleading during the formation evaluation step. The aim of this paper is to show through different case histories, how the various tool types can affect the formation evaluation model and a possible approach to mitigate the problem. The case study includes logs from different WLL and LWD tools, different well diameters, deviations and lithologies. After a careful QC, the data sets have been processed in order to identify homogeneous intervals (from the lithology and the porosity point of view) in order to make the response comparison based only on the characteristic of the different tool types. Once the conditions for a correct comparison have been fixed, the statistical distribution within the homogeneous intervals was quantitatively described using histograms and cross-plots. The results of the analysis have proven the influence of lithology: silty-shale sequences show the most significant discrepancy between the tool responses, while the clean lithologies show less or negligible discrepancy. If not properly considered, the different Density-Neutron (DN) tool responses inside a homogeneous interval can strongly affect the output of the petrophysical interpretation, mostly on shale volume and effective porosity with a dangerous fallout also in the reservoir modelling. In the presented case study, the observed discrepancy from different LWD Density tool can vary between 0-0.02 gr/cc while for Neutron tools between 0-5 p.u. To quantify the impact of the mentioned discrepancies on the petrophysical interpretation, a deterministic interpretation model (calibrated against cores data) was used for shale volume and effective porosity calculation. The comparison between DN shale volume derived from different tools and Gamma Ray (GR) has been defined as simple but strategic approach to understand the meaning of the DN response and therefore to define the proper shale point in the DN cross-plot for the petrophysical interpretation. If DN logs are acquired with various tool types properly calibrated, the discrepancy between the log readings is only due to the tool characteristics, and may mislead the interpretation if the lithological parameters are not properly calibrated. Comparison with other lithological logs (e.g. GR) and calibration with core data is mandatory for a correct and consistent petrophysical interpretation when different acquisitions are available.

In carbonate reservoirs, the estimation of a reliable permeability log is a long-standing problem mainly because of the inherent multi-scale heterogeneities. The conventional approach relies on core-calibrated algorithms applied to open-hole (OH) logs. In general, this static log-based prediction uses to underestimate the actual dynamic performance of the wells and an ad-hoc integration with production logging tool (PLT) and well test (WT) analyses represents a required step to correct the initial estimation. However, it is critical, and at once challenging, to define the relation between dynamic-based corrections and OH characterization outcomes. An elegant solution is here proposed that makes use of predictive analytics applied on special core analyses (SCAL), nuclear magnetic resonance (NMR) log modeling, and multi-rate PLT/WT interpretations. The methodology is presented for a complex oil-bearing carbonate reservoir and it starts with an advanced NMR characterization performed downhole for more than 100 wells, and after a rigorous calibration with SCAL. The main outputs are a robust porosity partition (in terms of micropore, mesopore and macropore contributions), and a physics-based permeability formula. Although the match with core data demonstrates the reliability of the applied NMR rock characterization, log permeability underestimates the actual dynamic performances obtained from WT, as expected. At the same time, multi-rate PLT data from more than 150 wells are used to compute an apparent permeability value for each perforated interval, automatically consistent with the associated WT interpretation. Finally, both static and dynamic characterization outputs are used as inputs for a dual random forest (RF) template. In detail, the first RF algorithm learns through experience how NMR porosity partition and core-calibrated permeability are related to PLT/WT apparent permeability values, after considering the proper change of scale. Next, the second RF is utilized to estimate the uncertainty associated to the previous step, still in a completely data-driven way. Hence, the so-defined dual model provides a continuous automatic flow-calibrated permeability log, together with its confidence interval, directly from static NMR responses. The presented methodology allows dynamic data to be incorporate efficiently into a static workflow by means of a pure data-driven analytics approach. The latter is able to shed light on the statistical relationships hidden in the available datasets, thus leading to a more accurate permeability estimation. It is also shown how this provides fundamental information for perforation strategy optimization and reservoir modeling purposes in such carbonate rocks.