well logging

Pandit Deendayal Petroleum University (PDPU) SPE Fest 2017 is the university's third International Oil and Gas Technical Festival under the maxim of "Irradiating Brilliance" and will be held 3–5 November. It is planned to be a platform for the coming generation of petroleum engineers to explore new horizons in the fields of oil and gas. The technical fest events will cover paper and poster presentations, model making, quiz competition, case study solving competition, virtual block bidding, well log interpretation, geology challenge, puzzle solving, drilling mud preparation, guest lectures, and spot trading competition. The events and competitions will give the participants insight into the petroleum industry. To help the participants interact, there will be networking and gala nights.

An international committee of reserves evaluation experts has completed the revision of the Petroleum Resources Management System (PRMS). The SPE Board approved it in June 2018. The updated PRMS is a consensus of input collected from consulting and financial firms, government agencies, E&P companies, a 90-day public comment period, and six sponsoring societies: the World Petroleum Council, the American Association of Petroleum Geologists, the Society of Petroleum Evaluation Engineers, the Society of Exploration Geophysicists, the European Association of Geoscientists and Engineers, and the Society of Petrophysicists and Well Log Analysts. Access the Revised Petroleum Resources Management System (PRMS).

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.

The identification of the fluid fill history is a necessity for the development strategy of any field, in particular in the Middle East where tectonic history is often reported to affect fluid distribution and contacts in many fields. The fluid fill concept for a low permeability carbonate field has been re-evaluated and modified from a tilted contact interpretation with imbibition of the deepest unit to a field-wide flat contact and primary drainage saturation distribution. The oil volumes in the reservoir under study are sensitive to minor changes in the structure and fluid fill due to the relatively low structural dip and low permeability transitional nature of the reservoir. The paper highlights the importance of removing preconceptions in data analysis and ensuring consistency on interpretations between different available data sources. It also demonstrates how data quality could completely change the fluid fill concept.

The three main reservoir units of the Lower Shuaiba A, Lower Shuaiba B and Kharaib have been charged from two oil migration events. Structural changes post the first primary drainage are revealed by regional seismic images of the shallower horizons. Due to the rock low permeability, the water saturations are above irreducible value and the whole interval is in the "transition zone". Kharaib unit was believed to be imbibed by the aquifer after charge and was not developed. Three possible fluid fill scenarios were investigated: a) tilted contact due to structural changes post-charge, b) imbibition of the deeper interval, c) primary drainage with field-wide flat contact related to the second pulse of charge. Each scenario impacts the development of the three units positively or negatively. Water saturation logs vs. True Vertical Depth plots were the main diagnostic tool used to rule out fluid fill scenarios. The plots were used to recognise lateral changes of the saturation profile and investigate imbibition signatures. Production data were also used to cross check the expected fluid fill scenario. The resistivity tools’ types and mud resistivities were examined.

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 has an objective of identifying the nature of formation fluid from an extreme tight fractured reservoir. A good understanding of petrophysical properties of the reservoir rock as well as the fluid it contains constitutes a real challenge for tight reservoirs, that are the most common unconventional sources of hydrocarbons. The front-line characterization mean is the Wireline logging which comes directly after drilling the well or while drilling, knowing that for low to extreme low porosity-permeability reservoirs any attempt of conventional well testing will not bring any added value not rather than a confirmation of reservoir tightness. A tailored workflow was adopted to design the most appropriate formation testing module, select the best depths for fluid sampling, and distinguish hydrocarbon from water bearing intervals. This workflow involves ultrasonic and Electric Borehole Images in combination with Sonic Scanner for natural fractures detection, localization and characterization, integrating Dielectric recording and processing for petrophysical evaluation, then Formation Testing was carried out for fluid identification and sampling. The use of borehole electric and sonic imager coupled with advanced sonic acquisition helped not only to identify the natural fractures depths, but also the nature of these fractures. This integration was used for selecting the sampling station.

Permeability and rock typing are two of the main outputs generated from the petrophysical domain and are particularly contributors to the highest degree of uncertainty during the history matching process in reservoir modeling, with the subsequent high impact in field development decisions. Detailed core analysis is the preferred main source of information to estimate permeability and to assign rock types; however, since there are generally more un-cored than cored wells, logs are the most frequently applied source of information to predict permeability and rock types in each data point of the reservoir model.

The approach of this investigation is to apply data analytics and machine learning to move from the core domain to the log domain and to determine relationships to then generate properties for the three-dimensional reservoir model with proper simulation for history matching. All wells have a full set of logs (Gamma Ray, Resistivity, Density and Neutron) and few have routine core analysis (Permeability, Porosity and MICP). On a first pass, logs from selected wells are classified into Self Organizing Maps (SOM) without analytical supervision. Then, core data is used to define petrophysical groups (PG), followed by linking the PG's to NMR pore-size distribution analysis results into pre-determined standard pore geometry groups, in this step supervised PGs are generated from the log response constrained by the relationship between pore-throat geometry (MICP) and pose-size distribution (NMR). Permeability-porosity core relationships are reviewed by sorting and eliminating the outliers or inconsistent samples (damaged or chipped, fractures or with local features). After that, the supervised PGs are used to train and calibrate a supervised neural network (NN) and permeability and rock type's relationships can be captured at log scale. Using dimensionality reduction improves the neural network relationships and thus data population into the petrophysical wells.

The result is a more robust model capable to capture over 80% of the core relationships and able to predict permeability and rock types while preserving the geological features of the reservoir. The application of this method makes possible to determine the relevance of core and log data sources to address rock typing and permeability prediction uncertainties. The applied workflows also show how to break the autocorrelation of variables and maximize the usage of logs.

This work demonstrates that the introduced data-driven methods are useful for rock typing determination and address several of the challenges related to core to log properties derivation.

Full field development of the Upper Jurassic carbonates, offshore Abu Dhabi is exceedingly challenging. The heterogeneous texture, complicated pore systems and intensive lithology changes all mark the regressive cycles of sedimentation. Such complicated characteristics obscure formation evaluation of these formations. Advanced well logging tools and interpretation methodologies are implemented to minimize the petrophysical uncertainties to qualify the products as field development critical elements. This case study highlights a newly applied NMR log interpretation approach. The results help to understand the complex pore system in a tight carbonate layer, along a horizontal drain drilled close to the oil-water contact.

NMR log data was acquired in real-time while drilling simultaneously with Gamma Ray, Resistivity and Image Logs. Earlier field studies recommended swapping standard T₂ free fluid relaxation cutoff values by actual laboratory NMR measurements for a higher precision suitable for the reservoir texture heterogeneity, the study itself supported the application of higher cutoff values to better discriminate the free fluid in well-connected macro pores from the irreducible which will have a direct impact on the computed permeability.

In this case study, a variable free-fluid T₂ cutoff was firstly implemented based on arbitrary estimations to match the computed Coates permeability to the offset core values. Free-fluid, irreducible fluids were sequentially computed. A unique NMR-Gamma Inversion (NMR-GI) workflow is further utilized as a mathematically defined approach to process the raw data using probabilistic functions. The result is a more precise pore size distribution, coherent with the geological variations. NMR Capillary pressure was computed.

The complex formation texture could be accurately tracked for thousands of feet drilled along the horizontal drain. After validation with offset core, the NMR-GI interpretation was combined with, Archie saturation and Image log analysis for a conclusive assessment. Hydraulic flow units were combined. Successful completion design and production zone selection articulated on the defined open hole log interpretation.

NMR while drilling logging and the applied (NMR-GI) methodology prove to be leading tools to assist in resolving carbonate reservoir complexities. Not only that they help to understand the pore system characteristics, but they effectively support well placement, completion and production.

Reservoir Rock Typing and saturation modeling need a two-sided methodology. One side is the geological side of the rock types to populate properties within geological concepts. The other side is addressing reservoir flow and dynamic initialization with capillary pressure. The difficulty is to comply with both aspects especially in carbonates reservoirs with complex diagenesis and migration history. The objective of this paper is to describe the methodology and the results obtained in a complex carbonate reservoir.

The approach is initiated from the sedimentological description from cores and complemented with microfacies from thin sections. The core-based rock types use the dominant rock fabrics, as well as the cementation and dissolution diagenetic processes. The groups are limited to similar pore throat size distribution and porosity-permeability relationships to stay compatible with property modeling at a later stage.

At log-scale, the rock typing has a focus on the estimation of permeability using the most appropriate logs available in all wells. Those logs are porosity, mineral volumes, normalized saturation in invaded zone (Sxo), macro-porosity from borehole image or Nuclear Magnetic Resonance (NMR), NMR T2 log mean relaxation, and rigidity from sonic logs. A specific calculation to identify the presence of tar is also included to assess the permeability better and further interpret the saturation history. The MICP data defined the saturation height functions, according to the modality of the pore throat size. The log derived saturation, and the SHFs are used to identify Free Water Level (FWL) positions and interpret the migration history.

The rock typing classification is well connected with the geological aspects of the reservoirs since it originates from the sedimentological description and the diagenetic processes. We identified a total of 21 rock types across all the formations of interest. We associated rock types with depositional environments ranging from supra-tidal to open marine that controls both the original rock fabrics and the diagenetic processes. The rock typing classification is also appropriate to model permeability and saturation since core petrophysical measurements were in use during the classification. The permeability estimation from logs uses multivariate regressions that have proven to be sensitive to permeability after a Principal Component Analysis per zones and per lithologies. The difference between the core permeability and the permeability derived from logs stays within one-fold of standard deviation as compared to the initial 3-fold range of porosity-permeability. We assigned the rock types with three Saturation Height Function (SHF) classes; (unimodal-dolomite, unimodal- limestone & Multimodal-Limestone). The log derived water saturation (Sw) from logs and SHF shows acceptable agreement.

The reservoir rock typing and saturation modeling methodology described in this paper are considerate of honoring geological features and petrophysical properties to solve for complex diagenesis and post-migration fluid alteration and movement processes.

Pore structure is of great importance in tight reservoirs identification and validation evaluation, especially for formations with developed fractured. However, the conventional pore structure evaluation method based on nuclear magnetic resonance (NMR) logging lost its role. This is because the fractures with width lower than 2mm did not have response in the NMR T2 spectrum. Whereas the porosity spectrum, which extracted from the FMI data, was considered to be effective in fractured reservoir pore structure evaluation. In this study, to quantitatively characterize tight glutenite reservoir pore structure in the Jiamuhe Formation in northwest margin of Junggar Basin, northwest China, 90 core samples were drilled for lab mercury injection capillary pressure (MICP) measurement, and the XRMI data (acquired by the Halliburton and be similar with FMI) was processed to acquire the porosity spectrum.