Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling

Haghighat, S. Alireza; Mohaghegh, Shahab D.; Gholami, Vahid; Moreno, David

doi:10.2118/169573-ms

Cited by 4 publications

(6 citation statements)

References 8 publications

(5 reference statements)

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“…Eventhough all of the four techniques of determining EUR ( see figure 24 ) show the same behavior in which EUR is greater when TOC has a higher percentage, the impact of TOC on EUR shows different individual results for each technique. For example, from the figure (24), formation with 3% TOC has a EUR of 2.1 Bcf by using TED, 4.2 Bcf by using PLE, 5.2 Bcf by using SPED and is showing more reasonable trend than the other models.…”

Section: Total Organic Carbon (Toc)mentioning

confidence: 89%

“…It is commonly believed to be true that a higher TOC value implies a greater production potentional for natural gas and oil. However, this positive correlation cannot be confirmed when considering the actual data which is represented by the grey dots which are the results of the conventional statistical analysis are used in shale gas reservoirs as noted in figure (24). In contrast, the red dots-line represents the Fuzzy Trend Recognition which successfully demonstrates the positive correlation between TOC and greater production potential for natural gas and oil.…”

Section: Total Organic Carbon (Toc)mentioning

confidence: 92%

“…Unlike conventional reservoirs, forecasting the future production of unconventional reservoirs is considered a big challenge because of the complexities in petrophysical and mechanical properties of these reservoirs. Therefore, using same conventional tools and techniques that have been used in conventional reservoirs to predict production in unconventional reservoirs is not completely successful idea [Mohaghegh, 2014]. Arps declared that the value of b has to be in range between zero to 1, but didn't discuss of getting b greater than 1.…”

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

“…In general, there are three techniques that used for forecasting such as Volumetric, Material balance and decline curve analysis. As mentioned earlier, using same conventional tools and techniques that have been used in conventional reservoirs to forecast production in unconventional reservoirs is not successful idea [Mohaghegh, 2014].…”

Section: Introductionmentioning

confidence: 99%

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Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters

Alabboodi¹

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In the last years, gas production from shale has increased significantly in the United States. Therefore, many studies have been focused on shale formation in different areas such as fracturing, reservoir simulation, forecasting and so on. Forecasting production or estimating ultimate recovery (EUR) is considered to be one of the most important items in the production development planning. The certainty in EUR calculation is questionable because there are different parameters that impact production and consequently the EUR such as rock properties and well completion design. Different methods to calculate EUR have been used in the industry. Traditionally, the decline curve analysis method by Arps (1945) was considered to be the best common tool for estimating ultimate recovery (EUR) and reserves. However, the Arps' equations over estimate of reserves when they are applied to unconventional reservoirs (extremely low permeability formation). The reason is that Arps' equations only work for Boundary Dominated Flow (BDF) decline. On the other hand, many research papers show that the production from the unconventional tight reservoirs is distinguished by an extended period of late transient flow, until reaching the boundary-dominated flow. To overcome these problems and improve the unconventional reservoir's production forecast, researchers have developed new empirical methods which are being implemented in all flow regimes.

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Section: Total Organic Carbon (Toc)mentioning

confidence: 89%

Section: Total Organic Carbon (Toc)mentioning

confidence: 92%

Section: Chapter 2: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters

Alabboodi¹

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“…Forecasting post-fracture deliverability of wells in Clinton Sandston gas storage field was done by Mohaghegh et al [37] [40] analyzed production behavior of 145 wells located in an unconventional asset in Wattenberg Field-Niobrara using wells' static (reservoir properties, well completion information) and dynamic data (operational information like days of production per month).…”

Section: -4-3 Neural Network Applications In Eandp Industrymentioning

confidence: 99%

Monitoring the Integrity of CO2 Storage Sites Using Smart Field Technology

Haghighat¹

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Monitoring the Integrity of CO 2 Storage Sites Using Smart Field Technology Seyed Alireza Haghighat Capability of underground carbon dioxide storage to confine and sustain injected CO 2 for a very long time is the main concern for geologic CO 2 sequestration. If a leakage from a geological sink occurs, it is crucial to find the approximate amount and location of the leak in order to implement proper remediation activity. An overwhelming majority of research and development for storage site monitoring has been concentrated on atmospheric, surface or near surface monitoring of the sequestered CO 2. This study is different it aims to monitor the integrity of CO 2 storage at the reservoir level. This work proposes developing in-situ CO 2 Monitoring and Verification technology based on the implementation of Permanent Down-hole Gauges (PDG) or "Smart Wells" along with Artificial Intelligence and Data Mining (AI&DM). The technology attempts to identify the characteristic of the CO 2 leakage by de-convolving the pressure signals collected at the Smart Well sites. Citronelle field, a saline reservoir located in Mobile County (Alabama, US) was considered for this study. A reservoir simulation model for CO 2 sequestration in the Citronelle field was developed and history matched. The presence of the PDGs were considered in the reservoir model at the injection well and an observation well. High frequency pressure data from sensors were collected based on different synthetic CO 2 leakage scenarios in the model. Due to complexity of the pressure signal behaviors, a Machine Learning based technique was introduced to build an Intelligent Leakage Detection System (ILDS). The ILDS was able to detect leakage characteristics in a short time (less than a day) demonstrating high precision in quantifying leakage characteristics subject to complex rate behaviors. The performance of ILDS was examined under different conditions such as multiple well leakages, cap rock leakage, availability of an additional monitoring well, presence of pressure drift and noise in sensor and uncertainty in the reservoir model. iii To my beloved wife Neda and my family Specially my father whose memory was my motivation iv ACKNOWLEDGMENTS I would like to express my appreciation and gratitude to all people supported me during my PhD. study. Special thanks and gratitude is for Dr. Shahab D. Mohaghegh, my supervisor, for involving me in this research. He is a dedicated instructor, supportive supervisor and sincere friend. Working with him will be remembered as a great experience. Great appreciation also goes to my committee members Mr. Ameri, Dr. Bromhal, Dr. Fathi and Dr. Wilson for their interest in this work and useful suggestions and comments that improved the quality of this dissertation. Special thanks to Mr. Eduardo Proano who traveled two times to Morgantown to join in my candidacy and final defense. I also would like to appreciate my teammates in Petroleum Engineering and Analytics Research Lab(PEARL), especially Vida Gholami and Alireza Shahkarami for ...

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Data-Driven Reservoir Management of a Giant Mature Oilfield in the Middle East

et al. 2014

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A novel approach to reservoir management applied to a mature giant oilfield in the Middle East is presented. This is a prolific brown field producing from multiple horizons with production data going back to mid-1970s. Periphery water injection in this filed started in mid-1980s. The field includes more than 400 producers and injectors. The production wells are deviated (slanted) or horizontal and have been completed in multiple formations. An empirical, full field reservoir management technology, based on a data-driven reservoir model was used for this study. The model was conditioned to all available types of field data (measurements) such as production and injection history, well configurations, well-head pressure, completion details, well logs, core analysis, time-lapse saturation logs, and well tests. The well tests were used to estimates the static reservoir pressure as a function of space and time. Time-lapse saturation (Pulse-Neutron) logs were available for a large number of wells indicating the state of water saturation in multiple locations in the reservoir at different times. The data-driven, full field model was trained and history matched using machine learning technology based on data from all wells between 1975 and 2001. The history matched model was deployed in predictive mode to generate (forecast) production from 2002 to 2010 and the results was compared with historical production (Blind History Match). Finally future production from the field (2011 to 2014) was forecasted. The main challenge in this study was to simultaneously history match static reservoir pressure, water saturation and production rates (constraining well-head pressure) for all the wells in the field. History matches on a well-by-well basis and for the entire asset is presented. The quality of the matches clearly demonstrates the value that can be added to any given asset using pattern recognition technologies to build empirical reservoir management tools. This model was used to identify infill locations and water injection schedule in this field. RESERVOIR MANAGEMENT Reservoir management has been defined as use of financial, technological, and human resources, to minimizing capital investments and operating expenses and to maximize economic recovery of oil and gas from a reservoir. The purpose of reservoir management is to control operations in order to obtain the maximum possible economic recovery from a reservoir on the basis of facts, information, and knowledge (Thakur 1996). Historically, tools that have been successfully and effectively used in reservoir management integrate geology, petrophysics, geophysics and petroleum engineering throughout the life cycle of a hydrocarbon asset. Through the use of technologies such as remote sensors and simulation modeling, reservoir management can improve production rates and increase the total amount of oil and gas recovered from a field (Chevron 2012). Reservoir simulation and modeling has proven to be one of the most effective instruments that can integrate data and expertise fr...

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Production Analysis of a Niobrara Field Using Intelligent Top-Down Modeling

Cited by 4 publications

References 8 publications

Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters

Conditioning the Estimating Ultimate Recovery of Shale Wells to Reservoir and Completion Parameters

Monitoring the Integrity of CO2 Storage Sites Using Smart Field Technology

Data-Driven Reservoir Management of a Giant Mature Oilfield in the Middle East

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