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We present a search engine method for finding approximate 1D solutions to circular central-loop configuration Transient Electromagnetic (TEM) data. The search engine method is a concept used for query search from a large database on the internet. By extension, several approximate solutions to an input TEM data can be searched rapidly from the pre-established database. While the database size is determined by the survey target and the sensitivity analysis of layers, the fast search speed is ensured by using the Multiple Randomized K-Dimensional (MRKD)-tree method in computer search technology. In addition to its high speed in finding solution, the search engine method gives a solution space, which quantifies resolution and uncertainty of the results. Combining both search and inversion methods can help to interpret TEM data reliably for near surface imaging.
Ross, E. (ASL Environmental Sciences, Inc.) | Loos, E. (ASL Environmental Sciences, Inc.) | Fissel, D.B. (ASL Environmental Sciences, Inc.) | Lapidakis, Y. (ASL Environmental Sciences, Inc.) | Zhang, O. (ASL Environmental Sciences, Inc.)
Completed and planned metocean and ice measurement programs off Greenland's eastern and western coasts result in large and varied datasets characterizing physical phenomena such as icebergs, sea ice, seabed scours, weather, surface waves, ocean currents, and water properties. Future planned measurement programs will expand on the spatial and temporal breadth of these datasets. Other datasets support the analysis of the measurement data including license area locations, bathymetry, glacier calving areas, and notable submarine features. In order to plan measurement programs, manage the acquired datasets, and use the data for characterization of the physical environment, a web-based geoportal was designed and developed. The geoportal aids scientists and engineers in their discovery and use of metocean and ice data. The geoportal development required balancing two aspects. Firstly, scientists and engineers have extensive needs to upload, organize, search, visualize, analyze, and download large and varied datasets. Secondly, there are inherent limitations of web technologies due to bandwidth, latency, and security constraints. Many design decisions were focused on balancing these issues and are presented here.
The efficiency of exploration is an intuitive concept to the explorationist. Factors that obviously contribute to efficiency include good geological interpretation, good geophysical measurements, and good interpretation of geophysical measurements. The size and type of structures present are also key determinants to efficiency. A present are also key determinants to efficiency. A numerical scale of efficiency is needed for the prediction of future success. Past exploration prediction of future success. Past exploration success within a search area is a result of two factors; one is the richness of the search area and the other is the high efficiency with which the past search was conducted. The dilemma in predicting future success is to resolve the degree in which each of these two factors accounted for past success. High past exploration efficiency in a low potential search area implies limited future success since most fields probably have been found already. Low past efficiency in a high potential search area implies that many fields remain undiscovered. The new search area analysis can resolve this dilemma by estimating the relative importance of each factor. This resolution is used in the prediction of future success.
This paper shows how to estimate past search efficiency based on the maximum-likelihood principle. While earlier papers involved the assumption that efficiency was constant over this history of exploration, this paper allows the efficiency to be different for different time periods. This will give a more realistic projection of future efficiency and, therefore, a better prediction of future exploratory success.
The most important single number required for the economic evaluation of oil and gas prospects is the probability of success. The estimation of this number is currently a very subjective process. Search area analysis gives a far more objective basis for estimating this number, utilizing the available information of exploration experience in the surrounding area. This greater objectivity can facilitate the formation of joint exploration ventures and the financing thereof because of the greater credibility of the economic evaluation. The method also provides a planning tool for evaluating exploration opportunity areas and for deciding which areas are best for the investment of resources.
The search area analysis was developed by the author and first published in 1972. The complete theoretical development is shown based on constant search efficiency. A more operational description of the method is given in Chapter IX of Ref. 3, where constant search efficiency also is assumed. The goal of the current paper is to replace the assumption of constant efficiency by that of constant efficiency within time periods. Periods of different efficiency are allowed, and the maximum likelihood estimates of these efficiencies can be obtained. This should give a more realistic projection of future efficiency and therefore a better prediction.
CONCEPTS OF THE SEARCH MODEL
The concepts of the model are simple. A search area is a basin, or a portion of a basin, within which the same geological and economic conditions persist. The search area contains a number of fields persist. The search area contains a number of fields (oil or gas), but the actual number of fields remaining undiscovered always is unknown. The fields are of various sizes. A very wide range of sizes is typical. For statistical purposes, field size is measured in terms of areal extent rather than reserves. For purposes of economics, an area-reserves relationship may be calculated. The exploration process is a process of selecting prospects within the search area. There is an prospects within the search area. There is an efficiency in the search process that is manifested in the prospect selection process. The efficiency is due to good geological interpretation and good geophysical techniques. At each point in time there is a next-best prospect in the search area. Exploration is a process of sequentially testing the next-best prospects. An efficiency of one would represent a purely random prospect-selection process. The chance of discovering each field would be proportional to the areal extent under random site proportional to the areal extent under random site selection.
Oil Search has identified four major exploration projects for natural gas on the heel of its recent Muruk Field discovery in Papua New Guinea. Two liquefied natural gas pools in the play are believed to contain reserves of 1 to 3 Tcf. Gas in the geologically complex region was found in both a hanging wall and a footwall reservoir. The Muruk-1 well is to be joined by several other wells throughout the next 2 years, depending on the findings of seismic interpretation analyses. Muruk-1 is operated by Oil Search, which holds a 37.5% stake in the project; ExxonMobil represents 42.5% and Santos the remaining 20% of the investment total.