Bigoni, Francesco (Eni S.p.A) | Pirrone, Marco (Eni S.p.A) | Trombin, Gianluca (Eni S.p.A) | Vinci, Fabio Francesco (Eni S.p.A) | Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Ali Hassan, Al Attwi Maher (ZFOD) | Ibrahim Uatouf, Kubbah Salma (ZFOD) | Bazzana, Michele (Eni Iraq BV) | Viviani, Enea (Eni Iraq BV)
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.
This paper discusses the use of a novel data-driven method for automated facies classification and characterization of carbonate reservoirs. The approach makes an extensive use of wireline and while drilling electrical borehole image logs and provides a direct and fast recognition of the main geological features at multi-scale level, together with secondary porosity estimation. This embodies an unbiased and valuable key-driver for rock typing, dynamic behavior understanding and reservoir modeling purposes in these puzzling scenarios.
The implemented methodology takes advantage of a non-conventional approach to the analysis and interpretation of image logs, based upon image processing and automatic classification techniques applied in a structural and petrophysical framework. In particular, the Multi-Resolution Graph-based Clustering (MRGC) algorithm that is able to automatically shed light on the significant patterns hidden in a given image log dataset. This allows the system to perform an objective multi-well analysis within a time-efficient template. A further characterization of the facies can be established by means of the Watershed Transform (WT) approach, based on digital image segmentation processes and which is mainly aimed at quantitative porosity partition (primary and secondary).
The added value from this data-driven image log analysis is demonstrated through selected case studies coming from vertical and sub-horizontal wells in carbonate reservoirs characterized by high heterogeneity. First, the MRGC has been carried out in order to obtain an alternative log-facies classification with an inherent textural meaning. Next, the WT-based algorithm provided a robust quantification of the secondary porosity contribution to total porosity, in terms of connected vugs, isolated vugs, fractures and matrix contribution rates. Finally, image log-facies classification and quantitative porosity partition have been integrated with production logs and pressure transient analyses to reconcile the obtained carbonate rock types with the effective fluid flows and the associated dynamic behavior at well scale.
The presented novel methodology is deemed able to perform an automatic, objective and advanced interpretation of field-scale image log datasets, avoiding time-consuming conventional processes and inefficient standard analyses when the number of wells to be handled is large and/or in harsh circumstances. Moreover, secondary porosity can be proficiently identified, evaluated and also characterized from the dynamic standpoint, hence representing a valuable information for any 3D reservoir models.
This paper discusses the enhanced use of noise logging aimed at characterizing the dynamics of complex reservoirs and addressing wellbore integrity issues. The methodology makes use of a fit-for-purpose quantitative spectral analysis of noise log measurements and can provide direct and fast information about well completion integrity, post-stimulation job efficiency, fluid flow path in the near wellbore region, reservoir porosity characteristics and flow-units identification.
The approach is presented by means of a study performed on several wells intercepting different heterogeneous reservoirs and characterized by complex completions and, sometimes, by intensive stimulation jobs. In details, a high-resolution noise pattern modeling in a wide frequency range is performed to discriminate the character of the recorded flow noise in terms of mesopores, macropores, fractures, behind-casing channels and completion elements (including active valves and leaking packers). In favorable scenarios, the noise power amplitude is also used to understand the contribution of active reservoir units.
It is proven that providing a quantitative noise pattern classification is fundamental to recognize unusual poor cement placement issues, not detectable by standard sonic and ultrasonic cement logs and to discriminate between leaking and sealing packers. Moreover, in case of acid and/or acid fracturing treatments in carbonate reservoirs, the methodology can identify the generated wormholes/fractures and quantitatively evaluate the efficiency of the stimulation jobs by means of noise power analysis in the related frequency range. In addition, a dedicated spectral noise modeling is also used in order to identify flow-unit contributions in multi-layer scenarios and the type of porosity providing the flow. The reliability of the workflow comes after a successful comparison with the available standard production logging interpretations. The integration of this approach with standard workflows completes the reservoir characterization providing additional dynamic outcomes.
The key role played by the enhanced modeling of spectral noise log data demonstrates the versatility of the methodology. Although the added values of this logging technique are already known, the quantitative use of noise power amplitude in selected frequency ranges is relatively new and can shed light on this topic for future advanced applications.
In new wells with hole stability problems, which require to be cased and cemented immediately, and in old wells without a complete or reliable log dataset, a formation evaluation by means of quantitative open-hole (OH) log interpretation is not possible. Cased-hole (CH) logging can represent the only solution, despite being usually considered highly uncertain. This paper discusses the standalone use of CH logs, integrated in a probabilistic framework, for petrophysical characterization and uncertainty propagation purposes.
The approach consists of a full statistical workflow aimed at a formation evaluation with only CH logs as input, also including capture cross-sections, carbon/oxygen ratios and inelastic spectra. Several wells with complete OH petrophysical characterization have been used to define the rate of success of this methodology in different scenarios. Furthermore, a Monte Carlo framework is introduced to account for the uncertainty quantification of the CH outputs. The final outcome is the set of probability distribution functions of the petrophysical properties, the most probable scenario and the associated uncertainty.
Three real operative applications, in scenarios with no OH logs available, are presented: an old well without a complete/reliable OH log dataset (gas- and oil-bearing sandstone reservoir with variable salinity), and two new wells with hole stability problems (in a sandstone and in a carbonate oil-bearing reservoir at high formation water salinity). In the latter cases, numerical simulations are performed to correctly handle mud-filtrate invasion effects for a robust modeling also in the shallow zone investigated by CH logs. In all these challenging conditions, a complete formation evaluation has been obtained, and successfully used to select proper intervals to perforate. The increased hydrocarbon production driven by the outcomes of the standalone CH characterization further validates the efficiency of this method.
Though CH logging is a well-known technology in reservoir monitoring, its successful standalone use for reliable formation evaluation can represent an important step forward in reservoir characterization processes, in all those cases where OH data are not available or too risky to acquire. Finally, the value of uncertainty analysis has a huge relevance for appropriate production optimization and reservoir modeling strategies.
Permeability estimation in carbonate reservoirs is challenging and it generally consists of core-calibrated algorithms applied on open-hole logs. Moreover, due to inherent multi-scale heterogeneities, apparent permeability from production logging tool (PLT) is usually necessary to let the static log-based prediction honor dynamic data. The correspondence between dynamic corrections and carbonate rock types is a long-standing problem and an elegant solution is presented by integrating advanced nuclear magnetic resonance (NMR) log modeling with multi-rate PLT interpretation.
The methodology, discussed on an oil-bearing carbonate reservoir, starts with a rigorous mapping between NMR responses and pore-size distribution, mainly determined by special core analyses (SCAL). Hence, a robust porosity partition template and a physically-based permeability formula are established downhole relying on the quantitative integration of SCAL and advanced NMR modeling. Multi-rate PLT and well test data are then analyzed to evaluate the boost needed for log permeability to match the dynamic behavior of the wells. Finally, porosity partition outcomes are used as pointwise predictors of dynamic permeability enhancement by means of a probabilistic approach.
In details, a system built upon mercury injection capillary pressure measurements, representative of the entire reservoir, shows a well-defined pore structure consisting of micropores, mesopores and macropores. At the same time, a quantitative link is established between NMR transverse relaxation time and pore-size distributions through an effective surface relaxivity parameter, both at laboratory and reservoir conditions. This allows discriminating micro, meso and macro-porosity downhole. Effective surface relaxivity also plays a critical role in the subsequent NMR permeability estimation based on a capillary tube model of the porous media and exploiting the full NMR/pore-size distributions. Although the match with core data proves the reliability of the comprehensive rock characterization, log permeability values underestimate the actual dynamic performances from well test. Therefore, the standard apparent permeability method from multi-rate PLT interpretation provides the necessary correction from the dynamic standpoint. Macro-porosity content is demonstrated to be the driver for a quantitative estimation of the excess in matrix permeability and an additional term complements the original NMR permeability predictor in order to honor the dynamic evidences. The approach makes use of a probabilistic framework aimed at considering the uncertainties in the a-priori simultaneous static and dynamic characterization.
The presented innovative methodology addresses the well-known issue of quantitatively incorporating dynamic log modeling into a purely static workflow, thus leading to a more accurate permeability estimation. This is fundamental for production optimization and reservoir modeling purposes in highly heterogeneous carbonate environments.
Raimondi Cominesi, Nicola (ZFOD) | Guglielmelli, Andrea (ZFOD) | Rotelli, Fabiana (ZFOD) | Putignano, Natale (ZFOD) | Roscini, Paolo (ZFOD) | Pirrone, Marco (Eni S.p.A.) | Galli, Giuseppe (Eni S.p.A.) | Vinci, Fabio (Eni S.p.A.) | Rametta, Dario (Eni S.p.A.) | Raniolo, Stefano (Eni S.p.A.)
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.
The definition of the actual net pay is one of the most important parameter for a comprehensive dynamic petrophysical reservoir characterization. In heterogeneous reservoirs, well test interpretation lacks a direct link with the real flowing thickness of the reservoir units and this represents the major cause of inaccurate permeability estimation. Usually production logging can represent the conventional way to collect this kind of information; however, in offshore deep water wells, these operations entail risks, costs and time.
The paper deals with a robust dynamic characterization approach based on a continuous temperature measurement performed during the whole well test operations in a deep water gas well. This novel technology provides a mapping of the flowing contributions thanks to temperature sensors integrated on the perforation guns overcoming risks and costs of a standard production logging tool (PLT) acquisition.
An accurate openhole logs static reservoir characterization was performed to drive the detailed flow allocation in the tested heterogeneous reservoir. In particular, resistivity invasion profiles, wireline formation test data and advanced nuclear magnetic resonance (NMR) interpretation provided a detailed petrophysical background on which a quantitative formation thermal evaluation was achieved. The time-lapse temperature analysis during the different well test periods (clean-up, draw-downs and final build-up) allowed the estimation of continuous logs of net pay, zonal contribution and effective permeability.
The application of this methodology, as well as mitigating time, risks and costs, is a driver to optimize well completion strategy and to calibrate the 3D reservoir model for a correct reserves allocation.
One of the most challenging issues to be addressed in reservoir characterization is the simultaneous definition of the static and dynamic rock properties in order to optimize the well completion and accordingly maximize hydrocarbon production.
While the estimation of static petrophysical properties comes from ad-hoc interpretations of well log data, the most common and practical way to collect information about dynamic downhole well behavior is Production Logging (PLT). This kind of data acquisition is commonly carried-out after the well test acquisition in order to provide an accurate contributing net pay value. Some static-to-dynamic frameworks have shown successful results (see more details in Pirrone et al, 2016) and have been also fruitful for the interpretation of what controls effective permeability in complex scenarios.
Nardiello, Roberto (Baker Hughes) | Kim, Yonghwee (Baker Hughes) | Chace, David (Baker Hughes) | Zhang, Qiong (Baker Hughes) | Galli, Giuseppe (Eni S.p.A.) | Pirrone, Marco (Eni S.p.A.) | Borghi, Massimiliano (Eni S.p.A.)
Evaluation of current reservoir pressure and depletion is important for gas-bearing sand reservoirs in mature fields. An understanding of pressure depletion profiles for unperforated multiple-stacked sands helps to avoid creating non-productive perforations. This paper presents a novel method to determine reservoir gas density and pressure to identify depletion in cased wellbores using multi-detector pulsed neutron log measurements. A thermal neutron capture cross section (Sigma) log is not sensitive enough to detect reservoir pressure changes. The method instead uses count rate ratio-based measurements exhibiting high sensitivity to pressure variations in gas reservoirs where water saturation changes are negligible.
Extensive Monte Carlo modelling is performed to predict tool responses based on reservoir characterization models for different gas densities. An iterative algorithm is applied until the calculated gas saturation equals the original saturation, and the current gas density and reservoir pressure are determined.
This paper also presents a sensitivity analysis using Monte Carlo stochastic simulation to address uncertainties in various parameters associated with challenging environments. Wellbores in mature fields with a long history of production may have complex completion geometries, uncertain wellbore fluid properties, and poorly defined fluid contacts. Formation heterogeneities of mineralogy and porosity are additional factors to be considered. Uncertainties associated with wellbore and formation variables must be assessed to obtain reliable reservoir depletion profiles. Sensitivity analyses using well and parameter-based Monte Carlo modeling have been performed and incorporated into the interpreted results. As a result, the impacts of those parameters on the estimated gas density and pressure are evaluated.
Application case studies from a mature gas field in Southern Europe are presented. The thick gas sand reservoirs were not affected by a water drive mechanism but only by pressure depletion and gas expansion (i.e., natural depletion drive). Original water in place for those sands was unchanged but pressure depletion was suspected. Consequently, estimating the degree of reservoir depletion became an important objective for production and reservoir management. Reliable results were successfully obtained with uncertainty assessments. Analysis of pressure depletion in the primary target formations of the field was used for subsequent reservoir management decisions.
Distal turbidites consist of thin laminations (inch scale), usually ranging from fine sand to clay-rich deposits and may represent major hydrocarbon reservoirs: Conventionally, they are studied by means of a log-based binary modeling that discriminates productive and nonproductive layers. Nevertheless, the binary model represents a major drawback when dealing with laminations in the silt-grain-size range, because their allotment to either end member can be extremely problematic. This paper deals with a novel, probabilistic, lithological facies- classification approach that integrates core data and a highresolution dielectric-dispersion wireline log: Its 1-in. vertical resolution and a related fit-for-purpose petrophysical model make the log tool’s response suitable to describe the lithological heterogeneity of these reservoirs. The approach is presented by means of a study performed on the cored section of a well drilled into a laminated gas-bearing Pleistocene reservoir in the Adriatic Basin. A core-based classification was first carried out with sedimentological descriptions, mineralogical analyses, cation-exchange-capacity (CEC) measurements, routine and special core analyses, and a statistical investigation of grain-size distributions: This allowed the identification of four lithofacies ranging from hemipelagite to coarse silt. Next, a log-based classification was carried out with a multivariate statistical numerical technique integrated in a Bayesian framework run on the dielectric-dispersion model curves. The outputs are the probability of log-facies, the most-probable facies scenario, and the associated uncertainty by means of entropy computation. In the end, a four-facies log-based classification was obtained that matches the core-based classification with an overall agreement in excess of 93%. Compared with the conventional methodology, the presented approach shows the added value of identifying intermediate lithologies, thus leading to a more-accurate quantification of the thickness of the potentially hydrocarbon-bearing net reservoir.
A novel probabilistic methodology for the estimation of the formation permeability, based on the integration of dielectric dispersion log measurements and near wellbore invasion modelling, is proposed.
A new-generation, high-resolution dielectric dispersion wireline log provides a reliable estimation of the main petrophysical parameters including porosity and water salinity. Due to a shallow depth of investigation, the tool measurements are strongly affected by the presence of mud filtrate in the flushed zone. In particular, dielectric-derived salinity can take advantage of a salinity contrast between water based mud and formation water showing a depth-by-depth characteristic salinity profile.
In turn, the salinity profile within the invaded zone is function of rock-fluid interactions, petrophysical properties and, in particular, of permeability. The novelty of the approach is to numerically simulate the mud invasion phenomenon and match the salinity profile provided by the dielectric dispersion modelling in order to estimate the formation permeability. In the proposed methodology the match of the salinity profile is obtained by integrating the near wellbore simulator with a probabilistic Bayesian algorithm.
The approach is presented by means of a study carried out on a cm-scale laminated gas-bearing reservoir in the Mediterranean offshore area. A complete set of wireline logs including dielectric dispersion measurements have been acquired and routine and special core analyses performed. The methodology provided a continuous quantitative estimation of permeability in the entire cored interval with the associated uncertainty. The match with core measurements proved very accurate even in this complex laminated formation.
In general, the new approach can be used to predict permeability in un-cored and un-tested wells, and, given the high vertical resolution of the dielectric tool, it is one of the few methods suitable for thin-layered reservoirs.
Quantitative permeability prediction is probably the most challenging issue in reservoir characterization and, at the same time, it is one of the most desired targets. According to conventional procedures, the estimation of rock permeability can be obtained either by laboratory analysis (at the plug centimetre scale) or through the interpretation of a well test or data from wireline formation testers, representative of the portion of the reservoir investigated during the test. However, cores and tests are not always available and, in general, the measurements are expensive and suffer from sampling and scale representativeness.
In this respect, big efforts have been deployed to obtain a reliable method for predicting permeability from well log data. With such indirect measurements, permeability can be inferred from a different property (e.g. porosity, nuclear magnetic resonance) and then extrapolated to un-cored/un-tested intervals and wells using models and assumption. However, as the models are not exact, the uncertainty attached to the results is high. The uncertainty is even higher in thin layered reservoirs characterized by bed thicknesses well below the resolution capabilities of standard logging tools. Nevertheless, since these reservoirs represent major gas reservoirs in the Mediterranean Sea, a proper characterization is mandatory (Chelini et al. 2009, Pirrone et al. 2011a, 2011b, 2014).