The Mishrif Formation is one of the important carbonate reservoirs in middle, southern Iraq and throughout the Middle East. In southern Iraq, the formation provides the reservoir in oilfields such as Rumaila/West Qurna, Tuba and Zubair. The top of the Mishrif Formation is marked by a regional unconformity: a long period of emersion in Turonian (ab. 4.4 My) regionally occurred boosted by a warm humid climate, associated to heavy rainfall. In Zubair Field, within the Upper interval of Mishrif Formation, there are numerous evidences of karst features responsible of important permeability enhancements in low porosity intervals that are critical for production optimization and reservoir management purposes. In the first phase, the integration of Multi-rate Production logging and Well Test analysis was very useful to evaluate the permeability values and to highlight the enhanced permeability (largely higher than expected Matrix permeability) intervals related to karst features; Image log analysis, on the same wells, allowed to find out a relationship between karst features and vug densities, making possible to extend the karst features identification also in wells lacking of well test and Production logging information. This approach has allowed to obtain a Karst/No Karst Supervised dataset for about 60 wells. In the second phase different seismic and geological attributes have been considered in order to investigate possible correlations with karst features. In fact there are some parameters that show somehow a correlation with Karst and/or NoKarst wells: the Spectral Decomposition (specially 10 and 40 Hz volumes), the detection of sink-holes at top Mishrif on the Continuity Cube and its related distance, the sub-seismic Lineaments (obtained from Curvature analysis and subordinately from Continuity), distance from Top Mishrif. In the light of these results, the most meaningful parameters have been used as input data for a Neural Net Process ("Supervised Neural Network") utilizing the Supervised dataset both as a Trained dataset (70%) and as a Verification dataset (30%). A probability 3D Volume of Karst features was finally obtained; the comparison with verification dataset points out an error range around 0.2 that is to say that the rate of success of the probability Volume is about 80%. The final outcomes of the workflow are karst probability maps that are extremely useful to guide new wells location and trajectory. Actually, two proof of concept case histories have demonstrated the reliability of this approach. The newly drilled wells, with optimized paths according to these prediction-maps, have intercepted the desired karst intervals as per the subsequent image log interpretation, which results have been very valuable in the proper perforation strategy including low porous intervals but characterized by high vuggy density (Karst features). Based on these promising results the ongoing drilling campaign has been optimized accordingly.
Zubair is a giant oil field located in the South of Iraq. The production started in 1951 and current oil production is around 450 kbopd achieved through 150 wells completed in two main formations: Mishrif (carbonate) and 3rd Pay (sandstone). The scope of this paper is to show how an integrated methodology based on core analysis, open-hole and cased-hole logs unlocked the underneath potential of a sand layer (L1) with an anomalous resistivity. Multiple wells, indeed, show resistivity curves in the L1 interval with surprising low values with respect to the average of other levels of the same sandstone reservoir. Therefore, fit-for-purpose open-hole (OH) and cased-hole (CH) log acquisitions have been integrated with information from cores and dynamic data (i.e. production logging) in order to better understand the phenomena behind the low resistivity scenario. As a consequence, several perforation extensions have been performed with L1 as the main target, providing an overall improvement of hydrocarbon deliverability without any increase in water production. In details, routine and special core analyses in L1 samples delineate the typical setting of a fine-grained low resistivity pay sandstone, able to host a large quantity of irreducible water. However, such behavior is not always present among L1 cores. Therefore, a methodology aimed at characterizing this sandstone behavior was mandatory. Nuclear magnetic resonance logging, commonly used to identify low resistivity pays, was not a suitable option due to bad-hole problems. Hence, an approach based on a detailed integration of OH resistivity and CH pulsed neutron logging (PNL) is used to recognize and characterize such low resistivity pay. This method mainly relies on the fact that formation water is very conductive and strongly affects the resistivity, while its effects on PNL measurements are not so pronounced. Such intuition is confirmed by multi-rate PLT interpretations that dynamically describe the L1 sandstone with fair productivity index and high reservoir pressure, together with a significant dry production contribution. In conclusion, a clear geological trend of L1 resistivity behavior is revealed and associated to the decreasing cementation of the matrix and its coarsening in the same direction. The integrated OH/CH methodology allows characterizing low resistivity intervals as pay zones. Such achievement represents an important milestone for the perforation strategy of new and existing wells in Zubair. As a natural consequence, the overall field production has been enhanced by widely applying the new technique without any increase in water-cut.
Carbonate reservoirs are often characterized by karst features occurrence, usually related to a significant permeability enhancement in presence of low porosity and low permeability matrix type sediments. The distribution of such karst features is generally highly heterogeneous and difficult to predict, making the reservoir management challenging. In Zubair Field (Iraq), there are numerous evidences of karst events within the Upper interval of Mishrif Formation. The production behavior of Upper Mishrif is therefore very heterogeneous, moving from wells with relatively low flow capacity, as expected from petrophysical interpretation, to wells with a very high flow capacity, hence related to karst enhanced permeability. The integration of petrophysical interpretation, well test and multi-rate production logging allowed to preliminary highlight the improved permeability intervals associated to karst. In addition, accurate image log analysis on the same wells investigated a possible relationship between vug densities and production data, to be extended also to wells lacking the latter data. This process allowed to define a karst flag in more than 60 wells. Then, correlations between karst features and different seismic and geological attributes were identified. The most meaningful parameters were used as input data for a Neural Net Process, leading to the definition of a probability 3D Volume of karst occurrence. The final outcomes of the workflow are karst probability maps, used as a driver for the definition of new wells targets and associated trajectories. The recent drilled wells, with optimized paths according to these prediction-maps, have demonstrated the reliability of this approach intercepting the desired karst intervals. This study represents a valuable opportunity in terms of understanding of the reservoir behavior and impact on the ongoing intensive drilling campaign and related field performance.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.