TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis paper describes an innovative Down Hole Permanent Monitoring System (PDHMS) that allows real-time monitoring of bottom-hole pressure and temperature of two stacked reservoirs using one vertical observation well in a Saudi Aramco field.Permanent monitoring of pressure and temperature enables reservoir engineers to assess the performance of the reservoir in areas such as flood front movement and pressure support maintenance. In this well a multi-reservoir dual gauge system was deployed to monitor pressure and temperature in two stacked carbonate reservoirs.The standard dual-gauge system mandrel architecture requires below packer installation of the gauges which in turn increases the risk of leakage in the electric lines of the system. In this paper, we describe an innovative and potentially reliable digital permanent monitoring solution that uses the state-of-the-art welded system that aims to eliminate the risk of leakage.Included in the paper are the design criteria, deployment methodology and the "lessons learned" from installation of this fully welded PDHMS.

The detection, characterization, modeling and impact of natural fractures in prolific producing reservoirs are major multidisciplinary challenges. The task, from fracture detection through modeling, is conducted conventionally through limited multidisciplinary integration resulting in poor fracture realizations and understanding. Currently, there is no industry standard workflow that encompasses the wide spectrum of multi-discipline natural fracture detection techniques and modeling approaches. Meanwhile, the availability and range of well performance and formation evaluation data combined with improved reservoir characterization (static and dynamic) techniques have raised the awareness of not only the extent of natural fractures characterization and modeling but also their impact on fluid flow, history match and prediction. This paper presents a workflow that integrates and leverages different field data types (static and dynamic) to strengthen natural fracture detection, characterization and modeling. In this approach, different data sources are combined and contrasted to derive a most likely natural fracture distribution, understanding and characterization in simulation modeling, including uncertainty range. This industry unique naturally fractured modeling workflow has been developed, adapted and enhanced through the collective experience of applying the Saudi Aramco, synergy-based "Event Solution" ( Elrafie et al. 2007) integrated reservoir study approach. This paper is supported by sanitized projects of large and mature producing reservoirs that firmly illustrate the success of this industry leading workflow and the impact of natural fractures on field production performance. Examples include; premature water breakthrough in reservoir regions and timescales that cannot be matched by matrix flow alone. Likewise, extreme oil rates that cannot be achieved through matrix permeability under measured pressure drawdown. These facts, coupled with static and geological insights are nested together to generate an integrated twenty-two components fracture detection and modeling workflow. Individually and in isolation, each data type component provides impractical and scattered fracture indications; however, the amalgamation of these varied data points narrows the uncertainty range of fracture realizations, yielding a robust and synergized fracture understanding and modeling. This paper outlines best practices and critical factors of naturally fractured reservoir modeling and dynamic simulation. This includes a unique way of representing natural fractures in the numerical simulation model. Since there are many types of fractures, in this paper to simplify the illustration of the workflows we will target only the fracture corridors or the cluster of sub-vertical fractures that are normally extensive in their length and sometimes associated with shear faults. KZ, Permeability in Z-Direction

The detection, characterization, modeling and impact of natural fractures in prolific producing reservoirs are major multidisciplinary challenges. The task, from fracture detection through modeling, is conducted conventionally through limited multidisciplinary integration resulting in poor fracture realizations and understanding. Currently, there is no industry standard workflow that encompasses the wide spectrum of multi-discipline natural fracture detection techniques and modeling approaches. Meanwhile, the availability and range of well performance and formation evaluation data combined with improved reservoir characterization (static and dynamic) techniques have raised the awareness of not only the extent of natural fractures characterization and modeling but also their impact on fluid flow, history match and prediction.

A detailed description of a modified Archie's equation is proposed to accurately quantify water saturation within low resistivity/low contrast pay carbonates. The majority of previous work on low resistivity/low contrast reservoirs focused on clastics, namely, thin beds and/or clay effects on resistivity measurements. Recent publications have highlighted a "non-Archie" behavior in carbonates with complex pore structures. Several theoretical models were introduced, but new practical applications were not derived to solve this issue. Built upon previous theoretical research in a holistic approach, new models and workflows have been developed. Specifically, utilizing a combination of machine learning algorithms, nuclear magnetic resonance (NMR), core and geological data, field specific calibrated equations to compute water saturation (Sw) in complex carbonate formations are presented. Essentially, these new models partition the porosity into pore spaces and calculate their relative contribution to water saturation in each pore space. These calibrated equations robustly produce results that have proven invaluable in pay identification, well placement, and have greatly enhanced the ability to manage these types of reservoirs. This paper initially explains the theory behind the development of the analysis illustrating workflows and validation techniques used to qualify this methodology. A key benefit performing this research is the utilization of machine-learning algorithms to predict NMR derived values in wells that do not have NMR data. Several examples explore where results of this analysis are compared to dynamic testing, formation testing and laboratory measured samples to validate and demonstrate the utility of this new analysis.