Metagenomics Microbial Characterization of Production and Process Fluids in the Powder River Basin: Identification and Sources of Problematic Microorganisms Associated with SWD Facilities

2021 ◽  
Author(s):  
Michael Enzien ◽  
Sadie Starustka ◽  
Michael Gurecki ◽  
Trinity Fincher-Miller ◽  
Bryce Kuhn ◽  
...  

Abstract Inconsistent bacterial control and monitoring led to variability in Salt Water Disposal (SWD) well performance and injectivity creating excess costs in biocide applications and remedial work. A metagenomics study using Whole Genome Sequencing (WGS) was conducted to determine the source(s) of problematic microorganisms throughout the process life cycle: Freshwater> Drilling> Completion> Flowback> Produced water> SWD. A total of 30 metagenomes were collected from the 6 process stages and identification and quantification of the major microbial taxa from each of these stages were identified. "Taxonomy to Function" associations were identified for all the major taxa found in the SWD fluids. WGS was performed on positive Sulfate Reducing Bacteria (SRB) and Acid Producing Bacteria (APB) media bottles inoculated in the field for a Flowback sample. Four of the six major taxa found in SWD samples are considered groups of microorganisms known to cause microbiologically influenced corrosion (MIC): Clostridia, methanogens, SRB and Iron Reducing bacteria. Thermovirga and Thermotagae, were the two most abundant taxa found in SWD samples, both thermophilic halophilic fermenting bacteria. The Fe reducing bacteria Shewanella was only detected in Drilling and SWD fluids suggesting its source was Drilling fluids. Completion fluid metagenome profiles from two separate sites followed similar patterns. During middle of completions Proteobacteria phyla were dominant taxa represented mostly by Pseudomonas. Other abundant phyla were all characteristic of polymer degrading bacteria. None of these taxa were dominant populations identified in SWD waters. Fresh water only shared similar taxa with Drilling and Completion fluids. A few minor taxa from Drilling and Completion stages show up as significant taxa in SWD fluids. The majority of taxa found in SWD samples appear to originate from Flowback and Produced waters, although at lower abundances than found in SWD samples. It cannot be determined if the microorganisms found in Flowback and Produced waters were endemic to the formation or come from contaminated source waters, i.e. process equipment used to store and transport water sources. Petrotoga mobilis was the dominant population of bacteria that grew in both media bottles, 96% and 77% for SRB and APB, respectively, while Petrotoga was detected at 14% in the field sample. The most abundant bacteria detected in field sample were Clostridia (38%) while only 2.7% were detected in APB media. SRB media bottle had 0.18% SRB detected by WGS; APB media had 9% SRB population abundance. No SRB were detected in corresponding field sample or below detectable limits (BDL) for WGS methods (<0.01%). WGS was forensically used to successfully identify type and source of problematic microorganism in SWD facilities. Results from media bottle and field sample comparisons stress the importance of developing improved field monitoring techniques that more accurately detect the dominant microorganisms.

Author(s):  
Carleton R. Bern ◽  
Justin E. Birdwell ◽  
Aaron M. Jubb

Comparisons of hydrocarbon-produced waters from multiple basins and experiments using multiple shales illustrate water–rock interaction influence on produced water chemistry.


Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 625
Author(s):  
Lijuan Chen ◽  
Bo Wei ◽  
Xianghong Xu

The influence of sulfate-reducing bacteria (SRB) on the corrosion behaviors of X80 pipeline steel was investigated in a soil environment by electrochemical techniques and surface analysis. It was found that SRB grew well in the acidic soil environment and further attached to the coupon surface, resulting in microbiologically influenced corrosion (MIC) of the steel. The corrosion process of X80 steel was significantly affected by the SRB biofilm on the steel surface. Steel corrosion was inhibited by the highly bioactive SRB biofilm at the early stage of the experiment, while SRB can accelerate the corrosion of steel at the later stage of the experiment. The steel surface suffered severe pitting corrosion in the SRB-containing soil solution.


2021 ◽  
Author(s):  
Pavel Dmitrievich Gladkov ◽  
Anastasiia Vladimirovna Zheltikova

Abstract As is known, fractured reservoirs compared to conventional reservoirs have such features as complex pore volume structure, high heterogeneity of the porosity and permeability properties etc. Apart from this, the productivity of a specific well is defined above all by the number of natural fractures penetrated by the wellbore and their properties. Development of fractured reservoirs is associated with a number of issues, one of which is related to uneven and accelerated water flooding due to water breakthrough through fractures to the wellbores, for this reason it becomes difficult to forecast the well performance. Under conditions of lack of information on the reservoir structure and aquifer activity, the 3D digital models of the field generated using the hydrodynamic simulators may feature insufficient predictive capability. However, forecasting of breakthroughs is important in terms of generating reliable HC and water production profiles and decision-making on reservoir management and field facilities for produced water treatment. Identification of possible sources of water flooding and planning of individual parameters of production well operation for the purpose of extending the water-free operation period play significant role in the development of these reservoirs. The purpose of this study is to describe the results of the hydrochemical monitoring to forecast the water flooding of the wells that penetrated a fractured reservoir on the example of a gas condensate field in Bolivia. The study contains data on the field development status and associated difficulties and uncertainties. The initial data were results of monthly analyses of the produced water and the water-gas ratio dynamics that were analyzed and compared to the data on the analogue fields. The data analysis demonstrated that first signs of water flooding for the wells of the field under study may be diagnosed through the monitoring of the produced water mineralization - the water-gas ratio (WGR) increase is preceded by the mineralization increase that may be observed approximately a month earlier. However, the data on the analogue fields shows that this period may be longer – from few months to two years. Thus, the hydrochemical method within integrated monitoring of development of a field with a fractured reservoir could be one of the efficient methods to timely adjust the well operation parameters and may extend the water-free period of its operation.


2021 ◽  
Author(s):  
Klemens Katterbauer ◽  
Waleed Dokhon ◽  
Fahmi Aulia ◽  
Mohanad Fahmi

Abstract Corrosion in pipes is a major challenge for the oil and gas industry as the metal loss of the pipe, as well as solid buildup in the pipe, may lead to an impediment of flow assurance or may lead to hindering well performance. Therefore, managing well integrity by stringent monitoring and predicting corrosion of the well is quintessential for maximizing the productive life of the wells and minimizing the risk of well control issues, which subsequently minimizing cost related to corrosion log allocation and workovers. We present a novel supervised learning method for a corrosion monitoring and prediction system in real time. The system analyzes in real time various parameters of major causes of corrosion such as salt water, hydrogen sulfide, CO2, well age, fluid rate, metal losses, and other parameters. The data are preprocessed with a filter to remove outliers and inconsistencies in the data. The filter cross-correlates the various parameters to determine the input weights for the deep learning classification techniques. The wells are classified in terms of their need for a workover, then by the framework based on the data, utilizing a two-dimensional segmentation approach for the severity as well as risk for each well. The framework was trialed on a probabilistically determined large dataset of a group of wells with an assumed metal loss. The framework was first trained on the training dataset, and then subsequently evaluated on a different test well set. The training results were robust with a strong ability to estimate metal losses and corrosion classification. Segmentation on the test wells outlined strong segmentation capabilities, while facing challenges in the segmentation when the quantified risk for a well is medium. The novel framework presents a data-driven approach to the fast and efficient characterization of wells as potential candidates for corrosion logs and workover. The framework can be easily expanded with new well data for improving classification.


2010 ◽  
Author(s):  
Mool Chand Nihalani ◽  
S. Verma ◽  
J. Kumar ◽  
H. Dubey ◽  
Nripendra Kumar Bharali ◽  
...  

2020 ◽  
Vol 174 ◽  
pp. 108855
Author(s):  
Nina Wurzler ◽  
Jan David Schutter ◽  
Ralph Wagner ◽  
Matthias Dimper ◽  
Dirk Lützenkirchen-Hecht ◽  
...  

Author(s):  
Qiaobo Zhang ◽  
Die liu ◽  
Yihao Liu ◽  
Hanjun Liu ◽  
Min Huang ◽  
...  

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