ABSTRACT

Azeri-Chirag-Guneshli (ACG) is a giant field located in the Caspian Sea, Azerbaijan, operated by BP Exploration (Caspian Sea) limited. The major reservoir zones comprise of sandstone formations with 20 to 25% porosity, 100 to 1,000 md permeability, and an oil column up to 1,000 m. Simultaneous development of several relatively young reservoirs by commingling production and injection in wells, the relatively high cost of intervention in the Caspian region, the configuration of platforms with restricted access to the well heads during drilling operations, and severely limited simultaneous rig and intervention operations, have been some of the key drivers in selecting, adopting and developing appropriate technologies for reservoir and well surveillance in ACG.

This paper describes the developments implemented in the field over the last 20 years to address the gaps in conventional technologies and enhance the efficiency of surveillance from:

  1. conventional production logging sensors (with surface- and memory-readout conveyance);

  2. array production logging, (3) the use of permanent downhole pressure-temperature gauges (PDHG); and

  3. permanent installations of distributed fiber-optic (DFO) cables for distributed temperature sensing (DTS) and distributed acoustic sensing (DAS). The paper also demonstrates how integration of data acquired from these technologies with data from other means of surveillance have helped improve our understanding of well and reservoir dynamics and subsequently, make important business decisions at different stages of field life.

The paper also discusses new methods developed for automated data streaming, visualization and interpretation with specific reference to (DFO) sensor data for downhole fluid-surveillance applications. The use of such techniques enables seamless integration and real-time interpretation of huge volumes (~1 TB/hr) of DFO data with other petrophysical surveillance data that can be fed into reservoir models allowing for improved reservoir management and proactive planning of well work activities. The diagnostic workflows are used take a holistic approach by integrating reservoir and wells data, both static and dynamic. Use of transient events is part of the method, complementing steady-state data. We show that well behaviors are better understood with permanent installations and continuous data acquisition, while intervention-type surveillance helps calibrate the models, all leading towards a representative flow diagnostic of well condition.

A retrospective view of more than two decades of flow-diagnostic examples in producer and injector wells is described in the paper, with key learnings on limitations, applicability and next developments is presented.

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