Single Well Microseismic Monitoring Leveraging Hybrid Cable Combining Both DAS and Traditional Geophones

The single monitoring well configuration is a favorable option for microseismic monitoring considering risk and cost. It has commonly been used in various industries for decades. When using a single monitoring well, we rely among other things on the waveforms’ polarization information to accurately locate detected microseismic events. Additionally, using a large array aperture reduces hypocenter's uncertainty. Instead of solely relying on 3C geophones to achieve such objectives, we propose to combine 3C sensors and distributed acoustic sensing (DAS) equipment. It is quite a cost-effective solution, and it enables us to leverage each system's strength while minimizing their respective limitations when considered individually. We present the technical feasibility of such a hybrid microseismic monitoring system using data acquired during a monitoring campaign performed in the Montney formation, Canada. In this dataset, the optic fiber (DAS) is located in the wireline cable used to deploy the 3C geophones; themselves located at the bottom of the DAS wireline cable. Though different acquisition systems are employed for the geophone array and the DAS array, both datasets are GPS time stamped so that data can be processed properly. We scan the DAS data using an STA/LTA event detection, and we integrate with the 3C geophone data. We find the microseismic waveform in both the DAS and the geophone sections and confirm the arrival times are consistent between DAS and geophones. Once datasets are merged, we determine hypocenters using a migration-based event location method for such hybrid array. The uncertainty associated with the event located using the hybrid DAS – geophone array is smaller than for any of the systems looked at independently thanks to the increased array aperture. This case study demonstrates the viability and efficiency of the next generation of a single well acquisition system for microseismic monitoring. Not only does it lower event location uncertainty, but it is also more reliable and cost-effective than the conventional approaches.

Remediation of Benzene and 1,2-Dichloroethylene in Groundwater by Funnel and Gate Permeable Reactive Barrier (FGPRB): A Case Study

Funnel and gate permeable reactive barrier (FGPRB) is an effective method to treat groundwater pollution. In order to clarify the impact of FGPRB on groundwater dynamic conditions, this study takes a site pilot test as the research object and establishes an FGPRB downstream of a petrochemical industry. The results show that the concentrations of 1,2-dichloroethylene and benzene in the downstream groundwater, after setting FGPRB, are lower than the detection limit. The numerical simulation results show that after setting FGPRB, both point source and area source pollution can achieve a good delay effect, extending from about 27 d to about 65 d of response time, but changing the thickness and permeability coefficient has no obvious effect on the delay effect. The tracer test shows the average permeability coefficient of the medium from the injection well to the monitoring well after the construction of FGPRB decreases from 77.0 m/d to 31.2 m/d after the construction of FGPRB. The average seepage velocity from the injection well to the monitoring well decreased from 0.19 m/d to 0.078 m/d after the construction of FGPRB. At the same time, when the FGPRB is not built, the maximum concentration time from the injection well to the monitoring well is about 10 d. After the FGPRB is constructed, the maximum concentration time of the tracer received by the monitoring well is about 27 days. These results confirm that the establishment of FGPRB will change the hydrodynamic conditions of groundwater and delay the response time of pollutants in the monitoring well.

Time series study for groundwater level evaluation in monitoring well

Climate and hydrogeological conditions of the Brazilian semi-arid demand sustainable and efficient water solutions. Groundwater monitoring programs are tools to subsidize the decision-making in this sense. In Ceará state, the monitoring of Araripe sedimentary basin aquifers is important for the development of the region. In this scenario, the present work aimed to study the groundwater level through an exploratory analysis of time series. The study area covered the eastern portion of the Araripe sedimentary basin, in the municipality of Milagres, in Ceará state. As the object of this study, it was obtained the time series of monthly average groundwater levels in a monitoring well of RIMAS/CPRM and installed in the Middle Aquifer System. Graphical and numerical methods were applied for the identification and description of time series main characteristics. Precipitation data in the study area were used to evaluate the system recharge. Results were discussed according to the environmental aspects of the study area. As a result, it was possible the identification and description of time series patterns such as trend and seasonality through the applied methods. It is also highlighted the sharp drawdown of groundwater levels in long term in the time series, reflecting the quantitative state of the aquifer system, as well as the groundwater recharge during the rainy season of the region, evidenced by the study of time series seasonality together with the precipitation data..

Groundwater Mapping of Total Coliform Contamination in Sleman, Yogyakarta, Indonesia

Groundwater is a pivotal resource to supply clean and drinking water besides surface water. In Indonesia, there are various issues regarding groundwater quality and quantity. By increasing population growth has an impact on groundwater quality, such as generated pathogen contamination. To prevent groundwater pollution was used sanitation facilities were called communal WWTPs. Unfortunately, most WWTPs were not performing optimal, and some had already stopped operating. This study aimed to display groundwater quality using the Inverse Distance Weighting (IDW) for 2018 to 2019 in the Sleman Region of Yogyakarta Province. The spatial analysis was completed by observation and interview methods. The total coliform number was measured from 29 monitoring wells. The results explained that compared to 2018, groundwater quality in 2019 showed a decrease in contamination by pathogenic bacteria. Groundwater flows also shown the spread of contamination from north to the south area. The distance between monitoring well and septic tank or WWTPs outlet, depth, and the physical condition were substantial factors for spreading the contamination.

A Closer Look at Hydraulic Fractures: Case Studies of Microseismic Results from Horizontal Monitoring Wells

Amongst different options of hydraulic fracture geometry detection or measurement, microseismic monitoring is a commonly used method to reveal the hydraulic fracture geometry in three-dimensional space. Microseismic monitoring typically requires one or several monitoring wells within an effective range from the treatment well, in which the geophones are set to detect the microseismic events occurring during or after the treatment. In the past, most of the monitoring wells have been vertical wells. We present several recent case studies in which both the treatment and monitoring wells were horizontal wells, which produced some unique and interesting observations beyond the initial expectations. One of the prerequisites of a proper microseismic monitoring of hydraulic fracturing treatment is to place the geophone in the proper position because a long distance between the actual fracturing events and the geophone may result in signal deterioration, which influences the processing and increases the uncertainty. This problem is more severe if the treatment well is a horizontal well because the distance from the geophone to the microseismic events varies between stages. One of the methods to solve this issue is to monitor the microseismic events in a horizontal offset well. As horizontal wells are often batched drilled in clusters for tight or unconventional resource nowadays, the availability of the monitoring well is less of a problem, and the constant distance from the monitoring well to the treatment well may help to generate better data quality and more accurate interpretation result. We implemented horizontal well monitoring in two difference cases between 2018 and 2019. For case A, one horizontal monitoring well was used to monitor 54 fracturing stages in three offset wells, and for case B, we monitored 24 fracturing stages in one offset well. In both cases, the geophone arrays were shifted in multiple positions to fit the distance requirements, and both cases generate satisfying interpretation results. The microseismic results from the two cases showed less uncertainty and better precision of microseismic events after processing, as we expected. What is surprising is this type of monitoring showed a unique physical phenomenon a couple of times, which is a casing background noise indicating excessive fracturing extension over a long distance. This phenomenon was captured in both cases, even with small injection rate and fluid volumes, which can be important information for us to better understand the dynamics of fracture propagation in such geomechanical environment and help to set a new guideline and design reference in the same region.

Robust CO2 Plume Imaging Using Joint Tomographic Inversion Of Distributed Pressure And Temperature Measurements

Geologic CO2 sequestration and CO2 enhanced oil recovery (EOR) have received significant attention from the scientific community as a response to climate change from greenhouse gases. Safe and efficient management of a CO2 injection site requires spatio-temporal tracking of the CO2 plume in the reservoir during geologic sequestration. The goal of this paper is to develop robust modeling and monitoring technologies for imaging and visualization of the CO2 plume using routine pressure/temperature measurements. The streamline-based technology has proven to be effective and efficient for reconciling geologic models to various types of reservoir dynamic response. In this paper, we first extend the streamline-based data integration approach to incorporate distributed temperature sensor (DTS) data using the concept of thermal tracer travel time. Then, a hierarchical workflow composed of evolutionary and streamline methods is employed to jointly history match the DTS and pressure data. Finally, CO2 saturation and streamline maps are used to visualize the CO2 plume movement during the sequestration process. The power and utility of our approach are demonstrated using both synthetic and field applications. We first validate the streamline-based DTS data inversion using a synthetic example. Next, the hierarchical workflow is applied to a carbon sequestration project in a carbonate reef reservoir within the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The monitoring data set consists of distributed temperature sensing (DTS) data acquired at the injection well and a monitoring well, flowing bottom-hole pressure data at the injection well, and time-lapse pressure measurements at several locations along the monitoring well. The history matching results indicate that the CO2 movement is mostly restricted to the intended zones of injection which is consistent with an independent warmback analysis of the temperature data. The novelty of this work is the streamline-based history matching method for the DTS data and its field application to the Department of Engergy regional carbon sequestration project in Michigan.

Checking up to keep on track: An Aboriginal-led approach to monitoring well-being

This article reports the process of identifying a well-being monitoring and evaluation approach for a community development programme with Aboriginal Native Title Holders in Northern Australia. The process involved the use of an empowerment-based Aboriginal Family Well-Being framework to enable Native Title Holders to articulate domains of value to their local community. These domains aligned with an existing culturally sensitive Aboriginal well-being survey tool which the Native Title Holders saw as relevant for their use. The attempts to provide Aboriginal people with a broader and more long-term perspective from which to judge the value of short-term projects is a different approach to traditional programme assessment (monitoring and evaluation). It aims to provide Aboriginal people with a more relevant frame from which they can make judgements about the worth of any programme or project in their location, supporting local control and decision-making. Potentially it provides Aboriginal people with the information from which to advocate for other supports and to assess the value of Government and other projects.

Machine Learning Provides Effective Leak Detection in Carbon Sequestration Projects

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 201552, “Leak Detection in Carbon Sequestration Projects Using Machine Learning Methods: Cranfield Site, Mississippi, USA,” by Saurabh Sinha, SPE, University of Oklahoma and Los Alamos National Laboratory; Rafael Pires De Lima, Geological Survey of Brazil; and Youzuo Lin, Los Alamos National Laboratory, et al., prepared for the 2020 SPE Annual Technical Conference and Exhibition, originally scheduled to be held in Denver, 5–7 October. The paper has not been peer reviewed. Saline aquifers and depleted hydrocarbon reservoirs with good seals located in tectonically stable zones make an excellent storage formation option for geological carbon sequestration.Ensuring that carbon dioxide (CO2) does not leak from these reservoirs is the key to any successful carbon capture and storage (CCS) project. In the complete paper, the authors demonstrate automated leakage detection in CCS projects using pressure data obtained from the Cranfield reservoir in Mississippi in the US. Results indicate that even simple deep-learning architectures such as multilayer feed-forward neural networks (MFNNs) can identify a leak using pressure data. Introduction Several methods that use different types of data currently are available to detect leaks. Although some of the methods are a direct indicator of CO2 presence, they cannot provide an early warning for the leaks, thus delaying remedial measures. An ideal process for the identification of leakages requires constant and repetitive comparisons of different data. Machine-learning (ML) techniques are ideally suited for this task. In this work, the authors demonstrate the use of ML techniques such as linear model, random forest, and MFNN on time-series signals obtained from a pressure-pulse test. The methodology uses the time-series data instead of 2D images or 3D voxels, thus providing a computational advantage. The authors write that an ML algorithm can distinguish between a pressure signal corresponding to a leak vs. the pressure signal corresponding to a baseline nonleak case. The trained models can then be used as an early-warning system to flag anomalous data to then be analyzed by a human interpreter. Background A pressure-pulse test uses at least two wells: an injection well and a monitoring well. The reservoir is then shocked by a series of predetermined cycles of injection and shut-ins (i.e., a pulse). The response then is recorded at the monitoring well with a pressure gauge that measures the target formation pressure. The test may be repeated with different pulses to understand the reservoir properties better. A harmonic pulse is preferred over a square wave because it allows for spectral decomposition of the pulse to analyze the reservoir response at different frequencies. Three wells are used in the study: F1, F2, and F3. Well F1 is the injector well, where alternative cycles of injection of CO2 and shut-in are carried out. Well F2 is the monitor well, which remains shut in for the duration of the test and where the pressure is monitored with the use of a pressure gauge. An artificial leak is simulated in the test by opening a surface valve at Well F3.