Advanced Reservoir Characterization in the Antelope Shale to Establish the Viability of CO2 Enhanced Oil Recovery in California's Monterey Formation Siliceous Shales, Class III

Morea, Michael F.

doi:10.2172/753983

2000

DOI: 10.2172/753983

|View full text |Cite

Advanced Reservoir Characterization in the Antelope Shale to Establish the Viability of CO2 Enhanced Oil Recovery in California's Monterey Formation Siliceous Shales, Class III

Michael F. Morea

Abstract: C02 injection profiles for the 12-8D and 12-7W wells. The tracks represent from left to righti gamma ray (25-75 API units), injection profiles, lithology, and resistivity (O-5 ohm m). The 12-8D shows the C02 injection profile (0-50'XO). The 12:7W shows, from left to right profiles for water injection (after C02), two CO~profiles (higher and lower rate), and three earlier water injection profiles (

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2017

2019

Publication Types

Select...

Other2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Neural Network Approach for Modeling Water Distribution System

Popa

O’Toole

Muñoz

et al. 2017

SPE Western Regional Meeting

View full text Add to dashboard Cite

The successful waterflooding field development depends not only of the understanding of reservoir characterization, sub-surface injection displacement and sweep-efficiency, but also an accurate and effective design and operation of the surface network’s water injection distribution. In certain cases, the latter is critical for the successful waterflood operations in large fields with thousands of wells and high volume of new development activity. For the purpose of this study, we are presenting a new data-driven approach to accurately estimate the injection rate in all non-instrumented wells in a large waterflooding operation. A collection of data driven tools, including statistics, clustering, simulation and an artificial neural network model were employed to prime and model the data. As a final point, the neural network leverages instrumented wells’ data and serves as an accurate real-time proxy to estimate missing injection rate measurements in non-instrumented wells. The system’s accuracy was validated by comparing the estimated rates for different wells on a different branch with the ones measured at physical wells. The neural network model trained on the cleansed data set revealed a high performance system with a >0.93 R2 values for both training and validation sets. The paper outlines both the methodology and procedures used to analyze a branch of the water network system, and the modeling of accurate estimation of injection rates. The model performance is remarkable having used only field and wellhead measured data and considering the natural uncertainty inherited in these values. Finally, this system provides the capability to estimate the flow rate for every non-instrumented well in the field and respond to exceptions in relevant time.

show abstract

A Neural Network Approach for Modeling Water Distribution System

Popa

O’Toole

Muñoz

et al. 2017

SPE Western Regional Meeting

View full text Add to dashboard Cite

show abstract

Improvement of Reservoir Management Efficiency Using Stochastic Capacitance Resistance Model

Kim

2019

SPE Western Regional Meeting

View full text Add to dashboard Cite

Lost Hills is a dual permeability Diatomite reservoir that is distinct from conventional reservoirs. Application of numerical simulation has been limited throughout field life due to the complex nature of the diatomite including low permeability (∼ 1 md) but high porosity (∼ 50%) and weak rock strength (∼ 100,000 psi of Young's modulus). Thus, many reservoir management practices are based on trial and error methods which are sub-optimal. This work aims to enhance the efficiency of reservoir management activities. The usage of Capacitance Resistance Model (CRM) has been increasing due to its simple but effective capabilities of analyzing waterflooding performances. However, CRM has innate limitations as well. The core calculation of CRM is solving nonlinear regression. Solution of nonlinear regression is not unique since it is not guaranteed to find the global minimum and is affected by solver algorithms and initial guesses. In addition to the innate limitations, due to the lack of bottomhole pressure data in the Lost Hills and its high oil viscosity (∼ 20 °API), the accuracy of Lost Hills CRM solution is not enough to be used in daily operations. Stochastic CRM (SCRM) was developed to mitigate these limitations by combining bootstrap with CRM and provides stochastic answers. SCRM estimates probabilities of an initial solution using bootstrap, substitutes low probable parameter values with P50 values, and updates injector-producer connection pairs and its interwell connectivity. SCRM was developed for analyzing waterflooding operations such as identification of ineffective injector-producer connection pairs and estimation of reservoir pressure. SCRM analysis results were benchmarked against the Lost Hills tracer test data and demonstrated that SCRM provided a better solution than CRM. Compared to the fact that the deterministic solution from CRM found only 50% of connection pairs which tracer identified, SCRM solution identified 10 out of the 12 tracer identified connections. After the verification, SCRM was applied to find out connected injectors which cause Fluid Over Pump (FOP) wells. The existing workflow for identifying connected injectors was a trial and error method and hard to find connected injectors if connected injectors are located farther than 300 ft from FOP wells (chronic FOP wells). The novel workflow has been deployed so that FOP wells can be mitigated systematically and enable the optimization team to improve its reservoir management efficiency. In 2017, 10 chronic FOP wells were mitigated by identifying connected injectors with the novel workflow.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Advanced Reservoir Characterization in the Antelope Shale to Establish the Viability of CO2 Enhanced Oil Recovery in California's Monterey Formation Siliceous Shales, Class III

Cited by 2 publications

References 0 publications

A Neural Network Approach for Modeling Water Distribution System

A Neural Network Approach for Modeling Water Distribution System

Improvement of Reservoir Management Efficiency Using Stochastic Capacitance Resistance Model

Contact Info

Product

Resources

About