The objective of this paper is to present a unique Petrophysical Grouping (PG) approach in a carbonate reservoir located in transition zone. It is very challenging, especially in Carbonate reservoirs, exist in transition zone, to establish PG definitions due to the complexities result from reservoir heterogeneities and diagenesis. Consequently establishing a suitable Saturation Height Function to match the Log derived Saturation is another challenge. In addition, the limited coverage of Mercury Injection based Capillary Pressure data (MICP) as compared to Routine Core analysis (RCA) data provides difficulties in establishing appropriate PG definition. In first step the MICP data was used together with porosity/permeability to define distinctive groups. The PGs were further up-scaled using deterministic and Neural Network (NN) approaches. The best method was chosen by performing a test that compares the Washburn Pore Throat Radius (PTR) with the predicted PTR. To estimate a most representative log based permeability model, independent of water saturation a NN and Self-Organization Map methodologies were adapted. The limitations of MICP samples were handled by using an analog of a larger field with 100s of MICP samples. This was used to propagate the PGs to log domain by utilizing the permeability model. Five PGs were defined using deterministic approach in which the best one is characterized having low displacement pressure, low irreducible water saturation, high pore throat radius and high porosity and permeability responses. Winland was shortlisted after testing other methods as the most applicable PG method in the reservoir as it provides the best correlation with lab PTR (94%) and the shape of WR35, consequently provides good match with computed Sw log and the shape of the PRT curve (Gunter, 2017). Regardless of the good response of NN approach the method was not chosen due to limitation of MICP data. A good relationship of Winland based PGs were found with geology and associated facies indicate strong affinity with the depositional environment and diagenetic overprints on each existing facie associations, hence a permeability model is depicted with confidence. The permeability model was executed for two geographic sectors using density, neutron porosity and GR as main inputs. SOM and NN Permeability were blind tested which resulted in more than 80% match. The predicted Sw matches with log based Sw over the entire field thus the PGs definition and propagation to log domain are considered valid.

Pulse neutron capture (PNC) is an effective technique to monitor lateral and vertical saturation/sweep. Assessing pulsed neutron results in either open-hole (OH) or cased-hole (CH) is key in evaluating formation properties, while with reservoir performance routinely monitored, time lapse logs are compared with the base logs to dynamically assess saturation changes and sweep efficiency. PNC sensitivity to factors such as invasion, bore-hole and cement; makes its result influenced by rock matrix and fluid properties. LWD logging may minimize the mud filtrate impact; however this is subject to the drilling parameters and exposure. LWD PNC has been analysed (drill and wipe pass) in both oil and water base muds. Quality assessment is performed on LWD/WL Sigma considering: mud used, invasion, formation water and mud properties. Variable Sigma was used to improve PNC saturation interpretation particularly within the transition zone. The effect of mud was discussed with examples showing its impact on data properties. WL PNC time lapse logging over five years were analysed, results indicated mud dissipation process masking formation response. Resistivity and pulse neutron saturations are compared. PNC saturation is integrated with resistivity based saturation. The capability of the pulsed neutron logging to derive meaningful results within the studied fields were examined in both OH, and CH conditions along with the definition of base log and the parameters used to derive saturation. Variable Sigma concept and uncertainty within transition zone is discussed. The used methodology and integration has improved the interpreted saturation, consequently enhanced the reservoir management. The data used within the paper comes from two different carbonate fields' of complex, heterogeneous pore structures, and diverse mineralogy, allowing generic approach to the drawbacks seen.

Technical evaluation and subsequently devising an appraisal and development strategy of a structural cum stratigraphic reservoir based on a discovery well only is always challenging. The reservoir under discussion was discovered as a structurally bounded trap and the appraisal wells were drilled on NW-SE direction along with the main bounding fault based on this understanding. However, presence of hydrocarbon below the spill point, anomalous sand thickness, lateral facies and reservoir quality variations observed in few of the wells indicated stratigraphic component in the field. Further complexity was added when the deepest tested gas was assigned on the structural map which showed extension of the hydrocarbon play outside the block boundary where the area was under different operating company that later drilled multiple wells near the block boundary. Therefore, it was critical to estimate correct initial gas in-place and percentage distribution of hydrocarbon across the lease boundaries. Figure 1 Well location map for the studied field Objective The objective of this paper is to present workflow that integrates multiple dataset to understand the field's hydrocarbon filling mechanism. Detailed geophysical and Petrophysical work has been carried out, which includes building of sequence stratigraphic framework, preparation of seismic attribute maps, understanding of the depositional setting for all the individual sand units encountered in all the wells, rock quality assessment (core and log methods with integration of capillary pressure curves), free water level (FWL) assessment, permeability modelling using machine learning approach (NN), pore throat radius estimation to relate hydrocarbon filling mechanism and saturation-height function modelling to build consistent 1D water saturation model. Datasets Integrated Comprehensive dataset has been acquired to evaluate the potential of the field that covers 3D seismic for the entire field, biostratigraphic analysis for seven (7) well, conventional logs in twelve (12) wells and advance measurements like Elemental Capture Spectroscopy and high-resolution resistivity images in five (5) wells. Core analysis data also acquired in five (5) different wells including routine core analysis, capillary pressure measurements using high pressure mercury injections, pore throat radius, relative permeability measurements (Centrifuge), formation resistivity factor measurements and sedimentological analysis (XRD & thin section) to overcome the challenges and defining the uncertainty associated with initial gas in-place. Methodology & Results Sequence based boundaries were defined to correlate individual sand bodies using the core data, image logs, elastic logs, seismic transacts and attribute maps for understanding the depositional setting. Lat-er these correlations were used to build a consistent petrophysical model including VCL estimation from Gamma/Neutron-Density/Sonic Density methods which was validated with ECS/XRD data. Porosity model was developed and validated from the core porosity followed by variable "m" estimation from the porosity/m relationship using the SCAL data. Later on, the consistent water saturation (Sw) models were built for all the studied wells. Permeability models were built using Neural Network (NN) where core-based permeability used for calibration and the model was tested qualitatively with the mobility and the well test permeability. For the validation of Sw from the logs, capillary pressure-based flow units were built using FZI/RQI, Winland & BVW (log) methods to define flow units defined through the core data. It was observed that the Winland R35 method-based pore throat radius had good correlation with the Sw log. FWL from MDT to estimate the height of the gas column, Skelt Harrison equation to capture the shape of the capillary pressure curve and Swi from the Centrifuge analysis were used to calibrate MICP end point which helped in building consistent Saturation-height functions. Results showed good to excellent match from the modeled Sw (Pc) vs Sw(log).

It is always a major challenge to estimate accurate initial water saturation in low relief/marginal fields. Several methods for calculating water saturation have been proposed in the industry, however each method has its limitation and relying on any single method will have direct impact on field development planning in early stages of the field. The objective of this paper is to present workflow that integrates multiple methods and help in defining uncertainties associated within each method to finalize water saturation model. Different methods such as, resistivity, dean stark, sigma, NMR and capillary pressure (Pc) approach were used to estimate accurate initial water saturation. The Sw resistivity method was computed using Archie's parameters such as; log derived deep resistivity and electrical properties from the SCAL data eg: cementation factor (m) and saturation exponent (n). The sensitivity analysis was performed to find which parameter contributes more to Sw uncertainty. Sigma method was used to compute Sw independent of resistivity. The main sigma inputs are sigma matrix, sigma hydrocarbon, sigma water and porosity. NMR was used to compute Swirr using total NMR porosity and free fluid porosity, it helps to bracket the minimum water saturation of the field. The capillary pressure approach was used for saturation height function (SHF) by grouping the Pc data into Petrophysical Rock Types (PRT). The Skelt Harrison function was used to fit Pc shapes observed in different rock types to develop SHF. The Sw sensitivity analysis indicates that water resistivity (Rw) and saturation exponent (n) have a minimal contribution to Sw uncertainty compared to the cementation factor (m), therefore variable m approach was used to narrow down uncertainty. Three different models for Sw_log were derived (minimum, base and maximum) as strong relationship were found between Petrophysical Groups (PGs) and m values. In order to get an accurate Sw_sigma computation, Sigma matrix and porosity indicate very little impact on Sw_sigma, whereas Sigma water found to be the highly uncertain parameter, due to the mixing of mud filtrate (Faisal. et. Al,. 2016) since the reservoir is in transition zone and the measurement's volume of investigation was shallow. Dean stark approach was also not reliable due to the invasion effects found. For the SHF, the Pc data was grouped into five PGs in which the best rock is characterized with a smaller (Pd), better pore size, and lower Swi. The workflow explains different ways and aspects of Swi estimation and gives interpreter the range of Swi uncertainty to do dynamic parameter sensitivity for optimum field development planning.