The interpretation and thickness prediction of seismic thin-bed is one of the difficult problems for geoscientists because of the band-limited property of seismic data. We propose a new thin-bed reflectivity inversion method based on the matching pursuit algorithm, a signal sparse decomposition method that is widely used in signal time-frequency analysis and image reconstruction. Different form the conventional matching pursuit algorithm, the new method firstly constructs a dictionary by using the synthetic seismic waveforms of a series of possible thin-bed reflectivity models, and then selects the optimum thin-bed reflectivity models through contrasting all the synthetic seismic waveforms in the dictionary with the real seismic waveform. To get a reliable result of inversion, priori information such as the number, the thickness and the reflecting intensity of the thin-bed can be used as constraint conditions during the inversion.
de Matos, Marci´lio Castro (Pontifi´cia Universidade Cato´lica &hyphen) | Oso´rio, Paulo Le´o Manassi (PUC‐RIO and Military Institute of Engineering) | Johann, Paulo Roberto Schroeder (PUC‐RIO)
We propose to use the matching pursuit with time-frequency dictionaries algorithm applied in each geological oriented segment of the temporal seismic trace jointly with the clustering of the Self Organizing Maps (SOM) as a new alternative to build seismic facies maps. The technique was applied to a real data from a deep-water field in the Campos Basin, Brazil.
Davidson, J.W. (Landmark Graphics Corporation) | Erdogan, M. (Landmark Graphics Corporation) | Dowty, T. (Consolidated Beef Producers Inc.) | DiPaolo, E.J. (Halliburton Energy Services) | Garrison, C. (Halliburton Energy Services)
Portfolio management is an ill-defined and, often, overused term. According to CIO magazine1, "ask seven Chief Information Officer's what portfolio management is, and you'll get ten answers." Whether a company's portfolio solution entails consolidating data into a spreadsheet or employing modern portfolio theory to develop an efficient frontier, the goal is to ensure long-term business success by optimizing return at the minimum risk.
In a recent survey, CIO Magazine2 explored factors influencing long-term business success. Magazine editors interviewed executives from 100 companies identified by them as the most innovative in the world. Many executives contended that success depends on uncontrollable influences such as macroeconomics and pure dumb luck, never the less, chances for success can be maximized by leveraging tools and processes to anticipate and capitalize on macroeconomic factors.
A proof-of-concept case study describing a project portfolio management engagement with a Fortune 500 client is presented. Engagement objectives were to develop a business screening and portfolio planning solution that
Minimizes reliance on luck
Provides a holistic view of all investment opportunities
Consistently quantifies risk
Leverages modern portfolio theory
Enables cash flow and revenue forecasting
Delivers the tools and processes for management to stabilize and grow revenue in a highly volatile market.
The business solution was applied to the client's 111 West Africa investment opportunities and entailed quantifying the risked value of investments, stochastically evaluating opportunities in a consistent manner, constraining the portfolio with investment thresholds, and portfolio optimization to balance risk and return.
The study demonstrates that project portfolio management can be a low-cost, high-return solution. Compared to the client's initial West Africa profit-to-investment portfolio (discounted P/I), the optimized case study portfolio (B) reduced Net Present Value (NPV) by 11% while eliminating 50% of the risk. Furthermore, opportunities were identified for immediate savings of $8 million dollars. The cost to develop and apply the solution was approximately $130,000.
Exploration and development of oil and gas resources is a fast-paced, continually evolving industry that experiences drastic changes in market conditions. Predicting growth, cash flow, operating income, resource requirements, and maintaining a competitive advantage in a volatile market is challenging for most companies. To optimize the chances for long-term success, a small percentage of companies are building competitive advantage by implementing tools, processes, and methodologies to facilitate the development of dynamic strategic plans that can be proactively adjusted to changing economic conditions. The feedstock for the strategic plan is an automated digital portfolio management system that provides a holistic overview of the global enterprise while delivering quantitative measures of risk and return under various conditions of capital constraint.