Processing systems/design

Well-Completion System Supported by Machine Learning Maximizes Asset Value

In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection.

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In this paper, the authors introduce a new technology installed permanently on the well completion and addressed to real-time reservoir fluid mapping through time-lapse electromagnetic tomography during production or injection. The variations of the electromagnetic fields caused by changes of the fluid distribution are measured in a wide range of distances from the well. The data are processed and interpreted through an integrated software platform that combines 3D and 4D geophysical data inversion with a machine-learning (ML) platform. The complete paper clarifies the details of the ML work flow applied to electrical resistivity tomography (ERT) models using an example based on synthetic data.

Introduction

An important question in well completions is how one may acquire data with sufficient accuracy for detecting the movements of the fluids in a wide range of distances in the space around the production well. One method that is applied in various Earth disciplines is time-lapse electrical resistivity. The operational effectiveness of ERT allows frequent acquisition of independent surveys and inversion of the data in a relatively short time. The final goal is to create dynamic models of the reservoir supporting important decisions in near-real-time regarding production and management operations. ML algorithms can support this decision-making process.

Methodology

In a time-lapse ERT survey [often referred to as a direct-current (DC) time-lapse survey], electrodes are installed at fixed locations during monitoring. First, a base resistivity data set is collected. The inversion of this initial data set produces a base resistivity model to be used as a reference model. Then, one or more monitor surveys are repeated during monitoring. The same acquisition parameters applied in the base survey must be used for each monitor survey. The objective is to detect any small change in resistivity, from one survey to another, inside the investigated medium.

As a first approach, the eventual variations in resistivity can be retrieved through direct comparison between the different inverted resistivity models. A different approach is called difference inversion. Instead of inverting the base and monitor data sets separately, in difference inversion, the difference between the monitor and base data sets is inverted. In this way, all the coherent inversion artifacts may be canceled in the difference images resulting from this type of inversion.

Repeating the measurements many times (through multiple monitor surveys) in the same area and inverting the differences between consecutive data sets results in deep insight about relevant variations of physical properties linked with variations of the electric resistivity.

Technology

The Eni reservoir electromagnetic monitoring and fluid mapping system consists of an array of electrodes and coils (Fig. 1) installed along the production casing/liner. The electrodes are coupled electrically with the geological formations. A typical acquisition layout can include several hundred electrodes densely spaced (for instance, every 5–10 m) and deployed on many wells for long distances along the liner. This type of acquisition configuration allows characterization, after data inversion, of the resistivity space between the wells with relatively high resolution and in a wide range of distances. The electrodes work alternately as sources of electric currents (Electrodes A and B in Fig. 1) and as receivers of electric potentials (Electrodes M and N). The value of the measured electric potentials depends on the resistivity distribution of the medium investigated by the electric currents. Consequently, the inversion of the measured potentials allows retrieval of a multidimensional resistivity model in the space around the electrode array. This model is complementary to the other resistivity model retrieved through ERT tomography. Finally, the resistivity models are transformed into fluid-saturation models to obtain a real-time map of fluid distribution in the reservoir.

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Fig. 1—The system installed on a production well.

 

The described system includes coils that generate and measure a controlled electromagnetic field in a wide range of frequencies.

Synthetic Tests

The geoelectric method has proved to be an effective approach for mapping fluid variations, using both surface and borehole measurements, because of its high sensitivity to the electrical resistivity changes associated with the different types of fluids (fresh water, brine, hydrocarbons). In the specific test described in the complete paper, the authors simulated a time-lapse DC tomography experiment addressed to hydrocarbon reservoir monitoring during production.

A significant change in conductivity was simulated in the reservoir zone and below it because of the water table approaching four horizontal wells. A DC cross-hole acquisition survey using a borehole layout deployed in four parallel horizontal wells located at a mutual constant distance of 250 m was simulated. Each horizontal well is a constant depth of 2340 m below the surface. In each well, 15 electrodes with a constant spacing of 25 m were deployed.

The modeling grid is formed by irregular rectangular cells with size dependent on the spacing between the electrodes. The maximum expected spatial resolution of the inverted model parameter (resistivity, in this case) corresponds to the minimum half-spacing between the electrodes.

For this simulation, the authors used a PUNQ-S3 reservoir model representing a small industrial reservoir scenario of 19×28×5 gridblocks. A South and East fault system bounds the modeled hydrocarbon field. Furthermore, an aquifer bounds the reservoir to the North and West. The porosity and saturation distributions were transformed into the corresponding resistivity distribution. Simulations were performed on the resulting resistivity model. This model consists of five levels (with a thickness of 10 m each) with variable resistivity.

The acquisition was simulated in both scenarios before and after the movement of water—that is, corresponding with both the base and the monitor models. A mixed-dipole gradient array, with a cycle time of 1.2 s, was used, acquiring 2,145 electric potentials. This is a variant of the dipole/dipole array with all four electrodes (A, B, M, and N) usually deployed on a straight line.

The authors added 5% of random noise in the synthetic data. Consequently, because of the presence of noisy data, a robust inversion approach more suited to presence of outliers was applied.

After the simulated response was recorded in the two scenarios (base and monitor models), the difference data vector was created and inverted for retrieving the difference conductivity model (that is, the 3D model of the spatial variations of the conductivity distribution). One of the main benefits of DC tomography is the rapidity by which data can be acquired and inverted. This intrinsic methodological effectiveness allows acquisition of several surveys per day in multiple wells, permitting a quasi-real-time reservoir-monitoring approach.

Good convergence is reached after only five iterations, although the experiment started from a uniform resistivity initial model, assuming null prior knowledge.

In another test, the DC response measured in two different scenarios was studied. A single-well acquisition scheme was considered, including both a vertical and a horizontal segment. The installation of electrodes in both parts was simulated, with an average spacing of 10 m. A water table approaching the well from below was simulated, with the effect of changing the resistivity distribution significantly. The synthetic response was inverted at both stages of the water movement. After each inversion, the water table was interpreted in terms of absolute changes of resistivity.

Conclusions

The technology is aimed at performing real-time reservoir fluid mapping through time-lapse electric/electromagnetic tomography. To estimate the resolution capability of the approach and its theoretical range of investigation, a full sensitivity analysis was performed through 3D forward modeling and time-lapse 3D inversion of synthetic data simulated in realistic production scenarios. The approach works optimally when sources and receivers are installed in multiple wells. Time-lapse ERT tests show that significant conductivity variations caused by waterfront movements up to 100–150 m from the borehole electrode layouts can be detected. Time-lapse ERT models were integrated into a complete framework aimed at analyzing the continuous information acquired at each ERT survey. Using a suite of ML algorithms, a quasi-real-time space/time prediction about the probabilistic distributions of invasion of undesired fluids into the production wells can be made.

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 197573, “Asset Value Maximization Through a Novel Well-Completion System for 3D Time-Lapse Electromagnetic Tomography Supported by Machine Learning,” by Paolo Dell’Aversana, Raffaele Servodio, and Franco Bottazzi, Eni, et al., prepared for the 2019 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, 11–14 November. The paper has not been peer reviewed.