A Novel Deep Reinforcement Sensor Placement Method for Waterfront Tracking

Waterfront movement in fractured carbonate reservoirs occurs in micro-fractures, corridors and interconnected fracture channels (above 5 mm in size) that penetrate the carbonate reservoir structure. Determining the fracture channels and the waterfront movements within the flow corridors is critical to optimize sweep efficiency and increase hydrocarbon recovery. In this work, we present a new deep reinforcement learning algorithm for the optimization of sensor placement for waterfront movement detection in carbonate fracture channels. The framework deploys deep reinforcement learning approach for optimizing the location of sensors within the fracture channels to enhance waterfront tracking. The approach first deploys the deep learning algorithm for determining the water saturation levels within the fractures based on the sensor data.. Then, it updates the sensor locations in order to optimize the reservoir coverage. We test the deep reinforcement learning framework on a synthetic fracture carbonate reservoir box model exhibiting a complex fracture system. Fracture Robots (FracBots, around 5 mm in size) technology will be used to sense key reservoir parameters (e.g., temperature, pressure, pH and other chemical parameters). The technology is comprised of a wireless micro-sensor network for mapping and monitoring fractures in conventional and unconventional reservoirs [1]. It establish a wireless network connectivity via magnetic induction (MI)-based communication since it exhibits highly reliable and constant channel conditions with sufficient communication range in the oil reservoir environment. The system architecture of the FracBots network has two layers: FracBot nodes layer and a base station layer. A number of subsurface FracBot sensors are injected in the formation fractures that record data affected by changes in water saturation. The sensor placement can be adapted in the reservoir formation to improve sensor data quality, as well as better track the penetrating waterfronts. They will move with the injected fluids and distribute themselves in the fractures where they start sensing the surrounding environment's conditions and communicate data, including their location coordinates, among each other to finally send the information in multi-hop fashion to the base station installed inside the wellbore. The base station layer consists of a large antenna connected to an aboveground gateway. The data collected from the FracBots network will be transmitted to the control room via aboveground gateway for further processing. The results exhibited resilient performance in updating the sensor placement to capture the penetrating waterfronts in the formation. The framework performs well particularly when the distance between the sensors is sufficient to avoid measurement interference. The framework demonstrates the criticality of adequate sensor placement in the reservoir formation for accurate waterfront tracking. Also, it shows that itis a viable solution to optimize sensor placement for reservoir monitoring. This novel framework presents a vital component in the data analysis and interpretation of subsurface reservoir monitoring system for carbonate reservoirs. The results outline the opportunity to deploy advanced artificial intelligence algorithms, such as deep reinforcement methods, to optimally place downhole sensors to achieve best measurement success, and track the waterfronts as well as determine sweep efficiency.

Well Integrity Catastrophe Avoided Through Advanced Well Integrity and Reservoir Monitoring Analysis, a Case Study

Well integrity is one of the main challenges that are facing operators, finding the source of the well problem and isolating it before a catastrophic event occurs. This study demonstrates the power of integrating different reservoir monitoring and well integrity logs to evaluate well integrity, identify the underlying cause of the potential failure, and providing a potential corrective solution. Recently, some Injector/producer wells reported migration of injection fluids/gas into shallower sections, charging these formations and increasing the risk of compromised well integrity. Characterization of the well issues required integration of multi-detector pulsed-neutron, well integrity (multi finger caliper, multi-barrier corrosion, cement evaluation, and casing thickness measurements), high precision temperature logs and spectral noise logs. After data integration, detailed analysis was performed to specifically find the unique issues in each well and assess possible corrective actions. The integrated well integrity logs clearly showed different 9.625-inch and 13.375-inch casings leak points. The reservoir monitoring logs showed lateral and vertical gas and water movements across Wara, Tayarat, Rus, and Radhuma formations. Cement evaluation loges showed no primary cement behind the first barrier casing which was the root cause of the problem. Therefore, the proposed solution, was a cement squeeze. Post squeeze, re-logging occurred, validating zonal isolation and a return of a standard geothermal gradient across the Tayarat formation. Most importantly, the cement evaluation identified good bond from the squeeze point clear to surface, isolating all formations. All these wells were returned to service (injector/producer), daily annular pressure monitoring confirmed that no further pressure build up was seen. Kuwait Oil Company managed to avoid a catastrophic well integrity event on these wells and utilized the approach presented to take the proper corrective actions, and validate that the action taken resolved the initial well integrity issues. Consequently, the wells were returned to service, and the company avoided a costly high probability blowout.

Reservoir Monitoring Activities for Co2 Wag Pilots

Two CO2 WAG Pilots are in progress in an Abu Dhabi Oil Reservoir. Each pilot has one horizontal producer and two horizontal injectors along with 2 vertical pilot observers to monitor the movement of flood front away from the injectors. The pilots are being monitored based on a detailed reservoir-monitoring plan. The paper discusses in detail various activities and the results related to the pilot monitoring. Methods, Procedures, Process The wells are being tested for oil rate, water cut, GOR on a daily basis using MPFM. For calibration purposes portable test separators are used every quarter to validate the rate, water cut and GOR measurements. Separator PVT samples from pilot wells are collected every quarter for PVT analysis. In addition PVT samples are also collected from the pilot wells and nearby wells every month from the sampling point near MPFM to monitor the CO2 content in the produced gas. Online CO2 analyzer is fitted on the surface flow line connecting pilot wells to the RDS to provide continuous measurement of CO2 in the produced fluid. Produced water is also sampled for detailed compositional analysis. Different gas and water tracers have been injected through the pilot injectors to trace the movement and breakthrough of injected fluids into the pilot producers. Sampling and analysis for tracer is carried out on a regular basis. Carbon and oxygen Isotope analysis for produced and injected CO2 gas is also carried out in order to monitor the breakthrough of injected CO2 into the pilot producers. There is a good difference in the carbon and oxygen isotopes of injected CO2 and the CO2 present in the reservoir. To monitor the changes in water and gas saturation with time across different layers a set of Pulsed neutron (RAS) logs are run in the observers on regular basis. PLT logs are run in the injectors and producers to check the distribution and conformance of the produced and injected fluids along the horizontal wellbore. Walk away VSP surveys are being carried out on regular intervals for one pilot to monitor the injected fluids distribution in the pilot area. The paper describes all these reservoir monitoring activities in detail. Results, Observations, Conclusions Analysis of Carbon oxygen RST logs are helpful for tracking fluid saturation changes and CO2 movement across the logged intervals. The RST logs in the observers demonstrate good sweep across different layers of the reservoir. Analysis of CO2 in produced gas has resulted into correctly pointing out the timing of CO2 breakthrough in the producers. It is well supported by the CO2 isotopes analysis for the injected and produced CO2 through pilot producer and nearly producers. The tracer analysis results show clearly the injector from where the injected CO2 has reached the producers. The PLT logs demonstrate good conformance for CO2 and water injection across the horizontal section in the injectors. All these monitoring activities provide a good source of data for further analysis and improved understanding of the pilots. Novel/Additive Information The paper discusses the usefulness of different reservoir monitoring tools for improved understanding of the pilots, which will be used as a basis for implementing CO2 WAG for the full area development.

In-Reservoir Waveform Retrieval for Monitoring at Groningen—Seismic Interferometry with Active and Passive Deep Borehole Data

The Groningen gas field in the Netherlands is an ideal test bed for in-situ reservoir monitoring techniques because of the availability of both active and passive in-reservoir seismic data. In this study, we use deconvolution interferometry to estimate the reflection and transmission response using active and passive borehole data within the reservoir at ∼3-km depth and separate up- and downgoing P- and S-wave fields by f-k filtering. We validate the results using synthetic data of a 1D elastic model built from sonic logs recorded in the well. The estimated full-waveform reflection response for a virtual source at the top geophone is consistent with the synthetic response. For the virtual source at the bottom geophone, the reflection response appears to be phase delayed, though its arrivals are consistent with the local subsurface geology. Similarly, the first-order estimated local transmission response successfully approximates that of the P-wave velocity in the reservoir. The study shows that reliable subsurface information can be obtained from borehole interferometry without detailed knowledge of the medium parameters. In addition, the method could be used for passive reservoir monitoring to detect velocity, attenuation, and/or interface time-lapse variations.