Downhole System Enables Real-Time Reservoir-Fluid-Distribution Mapping
You have access to this full article to experience the outstanding content available to SPE members and JPT subscribers.
This paper presents the basic concepts and architecture of the Eni Reservoir Electromagnetic Mapping (E-REMM) borehole electromagnetic (EM) mapping system that integrates borehole EM methodology with surface EM methods to provide real-time mapping of reservoir-fluid distribution during production or injection. By helping well teams know how distribution of hydrocarbons and water changes over time and space, the system addresses a fundamental requirement for managing the reservoir to maximize the recovery factor, optimize production, and reduce associated costs.
Several approaches are currently used to map hydrocarbons and other fluids in the reservoir in real time with variable sensitivity, differences in effectiveness, and a wide range of costs. Among the various approaches, EM and electric methods show a high benefit-to-cost ratio and a high sensitivity to the resistivity contrast between oil-saturated and brine-saturated reservoir rocks. These methods can estimate variations in fluid properties at reservoir depth in a distance range of hundreds of meters, although the spatial resolution decreases significantly with the distance between sources and receivers. Furthermore, combining multiple layouts in boreholes and at surface can improve the effectiveness of EM and electric methods significantly while increasing their spatial resolution.
The integrated system discussed in the complete paper allows multiscale EM prospecting and deep investigation in a large 3D volume of rocks between multiple wells and surface.
System Architecture and Functionality
The system consists of an array of electrodes and coils permanently installed in the production casing or liner, and the electrodes coupled electrically with the geological formations to enable the system to perform a quasicontinuous EM survey (Fig. 1).
When the electrodes and coils are powered, they generate, respectively, electric currents and a secondary EM field that propagate in the formation. Electric potentials and secondary currents are detected by other electrodes and coils, and their values depend on the resistivity distribution in the geological formation. Because hydrocarbons are far less conductive, and therefore more resistive, than common sedimentary rock impregnated with water, this type of measurement allows retrieval of information about fluid distribution in the reservoir.
Main system components, in addition to the downhole electrodes and coils, include the power-supply system for the source, the data-transmission system with necessary control electronics for feeding the EM devices, and all data-transmission cables.
Fig. 1 shows a simplified scheme with only four electrodes—AB for current injection and MN for electric potential measurement—and four magnetic coils. However, the system can include hundreds of EM devices permanently installed along the liner for long distances. The rationale is to record a large electric/EM data set using both a galvanic electric approach (with the electrodes) and an inductive EM approach (with the coils).
The complete paper includes a detailed description of the system components and architecture and technical constraints to ensure proper functioning. It also describes system installation.
Data Acquisition and Interpretation at Surface
Data gathered by the system are transmitted to the surface and transformed into resistivity models. These are interpreted in terms of fluid distribution in the reservoir, using empirical relationships between resistivity and fluid saturation. According to the authors, this approach provides important benefits in terms of production optimization and reservoir characterization, consequently delivering significant technical and economic advantages.
The acquisition can be performed almost without interruption during the various phases of hydrocarbon production or water injection. The entire data set is processed and interpreted to produce, almost in real time, a 4D resistivity model (3D resistivity distribution changing over time) (Fig. 2).
Using empirical relationships, such as Archie’s law or other formulas, the time-lapse resistivity models are transformed into space/time variations of the reservoir fluids distribution to provide near-real-time knowledge of how fluids move in the reservoir to help prevent such events as coning, cresting, and other undesired phenomena. This functionality is not currently achievable with any available geophysical or geological method, according to the authors.
Extended modeling and inversion of simulated data and laboratory and field experiments have been conducted in the last 3 years, demonstrating that the authors’ method can provide information about resistivity distribution and its time-lapse variations in the reservoir and in a large volume around it, up to distances of hundreds of meters. Independent field tests performed by other researchers fully support the basic principles of the EM approach.
Challenges and Benefits
Apart from the key geophysical issues (such as the maximum expected resolution and the maximum investigation range), the main challenges with the system to date are related to well-engineering issues. Completion technology must be designed to assure a good electric coupling of the electrodes with the geological formation. At the same time, electric insulation from the conductive casing must be guaranteed. This allows performing effective borehole electric resistivity tomography (ERT) surveys. A layout formed by multiple coils permanently installed on the same liner assures the acquisition of magnetic data, adding an inductive EM method to the ERT methodology. Use of both galvanic and inductive EM methods makes it possible to improve the robustness of the final resistivity models through two complementary approaches. Finally, the borehole system can be integrated fully with surface electric and EM layouts, further improving the robustness of the models and the investigation range.
Other benefits include the following:
- Optimization of production management, thanks to better mapping of fluids in the reservoir
- Maximization of hydrocarbon-recovery factor thanks to better planning of the recovery strategy
- Reduction of operational expenditures such as reduction of water-shutoff intervention
- Reduction of disposal costs for produced water
- Reduction of capital expenditures thanks to a modular approach to the asset-development strategy
- Identification of hydrocarbon accumulations supporting the processes of near-field exploration and in-field appraisal when the system is combined with surface layouts
- The system has no technical or economic limitation with regard to its suitability for different types of wells, reservoirs, and operational contexts. On the contrary, it will be applicable in all scenarios.
- Preliminary evaluations indicate that the system will deliver significant potential to change current hydrocarbon-asset-development and -management approaches. Examples of the areas of activity in which it could have a beneficial effect include:
- Modular developments
- Intelligent real-time production optimization
- Full control of the real path of the injected fluid or gas
- Reliable monitoring of carbon dioxide sequestration or injection
- Continuous dynamic reservoir modeling and production-profile updating
- Fulfillment of the desired maximum efficient rate
- Maximization of the total recovery factor
Downhole System Enables Real-Time Reservoir-Fluid-Distribution Mapping
01 September 2020
Artificial Intelligence in Operation Monitoring Discovers Patterns Within Drilling Reports
The complete paper provides an approach using machine-learning and sequence-mining algorithms for predicting and classifying the next operation based on textual descriptions.
Machine-Learning Approach Determines Spatial Variation in Shale Decline Curves
The complete paper describes an automated machine-learning approach to determine the spatial variation in decline type curves for shale gas production, based on existing data of production, completion, and geological parameters.
Machine-Learning Image Recognition Enhances Rock Classification
Automated image-processing algorithms can improve the quality and speed in classifying the morphology of heterogeneous carbonate rock. Several commercial products have produced petrophysical properties from 2D images and, to a lesser extent, from 3D images.
Don't miss out on the latest technology delivered to your email weekly. Sign up for the JPT newsletter. If you are not logged in, you will receive a confirmation email that you will need to click on to confirm you want to receive the newsletter.
19 October 2020
26 October 2020