To establish relationships between seismic derived acoustic impedance and LWD porosity measurements from several horizontal wells to be implemented into property modeling. This workflow is a sequential process that integrates property relationships from seismic scale to log scale using log data from a dozen of vertical wells and validate results at field scale with log data from about 50 horizontal wells. Overall process functions at grid-block scale in a 100x100mx1ft cell size following the four main phases. The first phase, involves exploratory data analysis and quality check. This is followed by a second phase of model building to concatenate all the required modeling steps. Third phase of model optimization explores the effect of all the parameters and data links defined in the process. Finally fourth phase involves validation to assess residual errors from the resulting porosity distributions and quantifying predictability of the model itself. A comprehensive and robust set of properties is generated by performing a recursive and convergent process of property modeling using lateral coverage from seismic inversion products and vertical resolution near well log scale. Independent analysis of different scales of porosity measurements are reconciled in this systematic approach by defining average distributions and descriptive statistics of reservoir properties at field scale. Variable data types, sample sizes and data resolution evolves across four different phases that integrates a holistic understanding of datasets in different dimensions. Quantitative analysis of seismic data ultimate correlates to a dense dataset from long horizontal wells. Final predictability of the model reaches a high confidence level (about 80% accuracy) when testing the predicted properties vs real measurements in about 50 horizontal wells. Multiple realizations of properties distribution matching all the available data is final output that provides a better understanding of reservoir property. This workflow allows total utilization of log data from horizontal wells into property distribution with no impact on overall statistics. No complex de-clustering operations are required as all the descriptive statistics are defined from vertical wells calibrated to core and seismic data. This methodology maximizes the value of LWD formation evaluation logs in property distribution, by combining the resolution of the logs along long horizontal wells with the strong lateral coverage of seismic inversion cubes.

This paper intends to utilise magnetic resonance (MR) data to predict the rock-type (RRT), by correlating its profile to the Mercury Injection Capillary Pressure (MICP), using a novel semi-automated method. Data presented come from Oilfield A, a reservoir highly affected by diagenesis causing reservoir properties to vary laterally. As a first step the feasibility of the method will be tested on the pilot well dataset, where its suitability will be assessed before applying the technique to the development well MR dataset. The fieldwide application in both pilot and development wells will improve the confidence of rock-type distribution. Two methods, normalised cumulative and cumulative are attempted, correlating the inferred NMR pore volume to the inferred MICP pore throat distribution, grouped according to the rock type scheme in two reservoirs. Both NMR and MICP data are arranged in the same format. On the normalised cumulative method, feasibility is conducted on MICP data to see at which normalised cumulative frequence the rock-type shows distinct pore throat clusters, while on the cumulative method the rock-type clustering is assessed on the cumulative frequency range at the total pore throat size. A key assumption is that the NMR relaxation profile representing the pore size, is similar to the MICP pore throat profile to classify the rock-type. QC is performed on the resulting curves to ensure they honour the reservoir property trends from core. One method will be chosen on this basis, where a set of criteria will be applied to the resulting curve to generate the first pass rock-type prediction. This paper contains the step 1 of rock-type estimation in the pilot wells and the suitability of application to the development wells. The development well application itself is currently still ongoing.

This paper presents a diagnostic workflow to understand and implement rock and fluid modeling in a diagenetically heterogeneous and hydrodynamically pressured Middle East carbonate field. The workflow allows interactive field data integration, provides guidance for reservoir property distribution and fluid contact generation in order to improve reserves and forecasting estimation. The workflow is useful to a reservoir modeler in QA/QC role and in this case it proves particularly applicable in an organization with constrained resources during the farm-in process. The workflow runs on numerical methods within the static model to avoid database discrepancy during the diagnostic process. Using the core (CCAL, SCAL), log and pressure database, the geoscientist can assess subsurface modeling outputs from the simplest to more complex deterministic scenarios. The process aims to minimize the discrepancy between data input and model output while continuously honoring the data, maintaining realistic correlations (e.g. between static permeability and water saturation) and respecting inherent uncertainty. Using a data-rich Middle East carbonate reservoir, the pre- and post-diagnostic comparison of 3D modeled reservoir properties to the input data are demonstrated. Diagnostic steps have helped to understand potential subsurface scenarios and thus minimize the discrepancy post exercise. The value of the workflow is its ability to pinpoint the key uncertainties in rock and fluid modeling from the field’s vast dataset in a shorter diagnostic time. The application of the workflow in this carbonate reservoir case study increases the importance of geological and property driven rock type classification and its 3D distribution in matching the water saturation profile. This proved particularly challenging in this case study due to the field’s compartmentalization - fluid contact scenario.

The rationale of structural uncertainty analysis in reservoir modeling is to quantify the range of probable Gross Rock Volume (GRV) s and searchfor the means to reduce this range as much as possible. This task considers running different scenarios and/or structural configurations based on the observed mismatch between structural depth estimation from seismic mapping and stratigraphic tops derived from well data. Integrated multi-disciplinary teams can collaboratively eliminate reservoir uncertainties at the well location, however uncertainty remains in the interwell area. The challenge for any reservoir characterization team is to share expertise across disciplines in order to mitigate the lack of information with scientific reasoning. In this way the range of uncertainties impacting business decisions, development scenarios or data acquisition plans are minimised. The workflow summarized here is an example of how to utilize structural elements from existing wells to quantify intrinsic GRV uncertainty while building static models. Offshore Field developments usually have a bigger horizontal well count than the ideal vertical penetrations and this case study is no exception in this case study. The ultimate goal of this publication is to generate the inputs required for a more realistic set of structural realizations that fulfil all of the current understanding from horizontal well placement and their intrinsic structural uncertainty.

This paper summarizes an efficient workflow for building a reliable static model reference case by improving the accuracy of well placement in a hydrocarbon bearing structure. This is beneficial in optimising upcoming well target position and trajectory planning as well as during the dynamic history matching process. In a non-operated venture, the ability to generate an up-to-date static model that maintains pace with operations, provides valuable insight to advise the operator on the upcoming drilling plan and continuously supports the dynamic model for reserves booking, is highly sought after. The systematic approach described in this paper is applied to a geo-model from a Middle East carbonate reservoir consisting of over 50 wells with good quality PSDM seismic data. The workflow presented begins with seismic mapping, utilizing volume-based modelling techniques, followed by structural element correction using borehole images (e.g. structural formation dip and true stratigraphic thickness estimate) and finally introduces alternative control points, which enable drilled wellbore trajectories to be structurally anchored, based on layer thicknesses and structural trends within the target reservoir. Using this approach it is possible to generate a consistent structural model that honours geological markers, measured dip ranges and structural trends seen from seismic data and image logs. During the process one learns more about data quality (e.g. scale of data resolution and depth of investigation), associated with specific fields and carbonate reservoirs through the interaction between geological, geophysical and petrophysical disciplines and ensures their correct use. Data are used to improve the raw interpreted seismic horizons by calibrating mapped thickness distribution against the well tops. 2D visualizations are generated on a well-by-well basis, including map views, curtain sections (along each horizontal well), composite cross-sections and 3D visualizations to show inter-well relationships within different geological layers. As a result the well is placed in the correct structural position. Correct well placement, especially of highly deviated/horizontal wells, provides more accurate identification of reservoir sweet spots, leading to improved well target position and trajectory planning for upcoming wells, and a robust baseline to achieve production/well test history match during the dynamic modelling process.