Solution Enables Well Surveillance in Stacked Reservoirs With Near-Critical Fluids
Authors: Carpenter Chris 
Published: September 2024

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23912, “Digital Solution for Well Surveillance in Stacked Reservoirs With Near-Critical Fluid Systems,” by Kristian Mogensen, SPE, Carlos Mata, SPE, and Swarjit Samajpati, ADNOC, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference.

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Fully compositional integrated asset models (FC-IAMs) are being deployed for an increasing number of fields in the operator’s portfolio. The complete paper describes the development of comprehensive digital well surveillance and field-production optimization for an offshore field consisting of four stacked reservoirs, each containing near-critical fluids. Augmenting the FC-IAM with high-frequency sensor data in addition to proprietary tools to actively monitor well performance helps identify and pursue opportunities to maximize oil production from the field, subject to several system constraints.

Introduction
The field is approximately 120 km offshore Abu Dhabi. During the appraisal phase, eight wells penetrated the structure and confirmed that the predominant lithologies are composed of dolomite and anhydrite, with subordinate limestone. Production of oil and gas comes from four main reservoirs—A, B, C, and D. The ongoing development centers around miscible gas injection along the crestal part of each reservoir and peripheral water injection.
Integrated asset models are one of many key initiatives supporting the operator’s Smart Production Growth initiative. Although the calculations identifying scope for improved production rates are firmly founded in science, substantial digitization efforts are required to automate, streamline, and sustain any such digital solution. Fig. 1 illustrates the fact that company database management plays a fundamental role in providing reliable input to keep models updated and fit for purpose.

Fluid-Property Modeling
Each of the four producing reservoirs contains near-critical fluids exhibiting a steep compositional depth gradient, which means that the solution gas/oil ratio (GOR) is well-specific and may change over time. The GOR trend is governed by several factors. The lightest part of the fluid column may be produced first because of the improved mobility; this would lead to a decreasing solution GOR over time. However, because of the crestal miscible gas injection, significant swelling will result in a gradual GOR increase followed by gas breakthrough.
Solution GOR increases because of gas swelling. It is important to make the distinction between solution gas and free gas because the operational guidelines dictate that the reservoir pressure should be maintained above the saturation pressure and above the minimum miscibility pressure.

Well-Test Validation Workflow
The compositional framework adds an extra layer of complexity because fluid composition needs to be specified at the well level. A robust procedure is needed to first assign an initial reservoir fluid composition to each well. The second key task is to capture changes over time.
The method of solution is referred to as a target GOR calculation. For any given fluid system, an equation of state (EOS) model must be tuned to experimental data to convert compositional information to black-oil properties at specific pressures and temperatures. A so-called anchoring fluid composition initially is assigned to the well, and the EOS can then calculate the resulting GOR and other variables from a simulated separator test. The evolved gas and liquid phases are then recombined mathematically to match a measured GOR from a production test. Matching is an iterative process but normally converges very quickly.