Tailoring the TLP – Evolution and Adaptation of the Concept
Abstract: TX 75083-3836 U.S.A., fax 01-214-952-9435.Abstract A generic characteristic of the Tension Leg Platform (1LP) concept is the interdependencies between the elements of its design, Ref. 1. For example, a change in deck layout and/or weight can affect the center of gravity, which affects platform dynamics, tether tensions, and so on. An important attribute of the concept is its insensitivity to water depth while providing direct access to wells for maintenance.TLPs can be configured in a variety of ways to achiev… Show more
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Cited by 2 publications
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Cost models for deepwater oil and gas facilities can be valuable tools for concept comparison and selection, field development planning and optimization, and in benchmarking performance. This paper estimates cost models for spar and tension leg platform projects using public and private data on 24 major projects. In addition to providing an analysis of the variables that affect cost, the paper investigates the complexity of regression model specification in a decision-making setting. We evaluate sensitivity to modeling assumptions, sample selection bias, and other model specification issues. The evidence suggests that sample selection bias is not a significant problem in cost models for spars, but that it is potentially significant in total cost models for TLPs. Introduction Cost models for deepwater oil and gas facilities have a variety of uses. Cost models can be employed early in prospect development to estimate expected lease values, to prepare bidding strategies, and to select prospects and manage the portfolio. As exploration prospects mature into defined development projects, cost models can be used to inform concept comparison and selection, to aid in field development planning and optimization, and to benchmark projected and actual costs. Given these virtues, it is surprising to find so little in the offshore production facility literature on empirical cost models or cost modeling methods. We believe that the primary causes of this gap are (i) the proprietary nature of cost and technical data that greatly increases the effort required to collect the data for detailed models, and (ii) the underlying nature of the facility selection decision process that results in a non-trivial sample selection problem. We address both of these issues in this study. First, we use private cost data carefully organized by industry experts based on public information and interviews with operating companies. The specifications are parsimonious, allowing us to use publicly available technical data, but without a sacrifice in model explanatory power. Second, we define a constrained facility choice model that facilitates diagnosis and treatment for sample selection. This case study estimates cost functions for spars and tension leg platforms (TLPs) using data from 24 completed projects. A two-stage regression model is specified that accounts for the underlying facility selection process. Publications that examine design optimization and cost estimating for specific technologies and/or projects are abundant (see for example Brooks and Carroll (1994), Zimmer (1994), and Stokes et al. (1996)). The gap we are referring to is in the area of aggregated analysis across projects and operators; the most recent published study known to the authors is Karlik (1991). Sample selection problems in statistics and regression analysis occur when the sampling is not random. In this case study, the samples of spars and TLPs are the result of a selection process assumed to be based on profit maximization. Therefore, the observations cannot be construed as a random sample and additional computations are required to correct for this feature of the data.
Cost models for deepwater oil and gas facilities can be valuable tools for concept comparison and selection, field development planning and optimization, and in benchmarking performance. This paper estimates cost models for spar and tension leg platform projects using public and private data on 24 major projects. In addition to providing an analysis of the variables that affect cost, the paper investigates the complexity of regression model specification in a decision-making setting. We evaluate sensitivity to modeling assumptions, sample selection bias, and other model specification issues. The evidence suggests that sample selection bias is not a significant problem in cost models for spars, but that it is potentially significant in total cost models for TLPs. Introduction Cost models for deepwater oil and gas facilities have a variety of uses. Cost models can be employed early in prospect development to estimate expected lease values, to prepare bidding strategies, and to select prospects and manage the portfolio. As exploration prospects mature into defined development projects, cost models can be used to inform concept comparison and selection, to aid in field development planning and optimization, and to benchmark projected and actual costs. Given these virtues, it is surprising to find so little in the offshore production facility literature on empirical cost models or cost modeling methods. We believe that the primary causes of this gap are (i) the proprietary nature of cost and technical data that greatly increases the effort required to collect the data for detailed models, and (ii) the underlying nature of the facility selection decision process that results in a non-trivial sample selection problem. We address both of these issues in this study. First, we use private cost data carefully organized by industry experts based on public information and interviews with operating companies. The specifications are parsimonious, allowing us to use publicly available technical data, but without a sacrifice in model explanatory power. Second, we define a constrained facility choice model that facilitates diagnosis and treatment for sample selection. This case study estimates cost functions for spars and tension leg platforms (TLPs) using data from 24 completed projects. A two-stage regression model is specified that accounts for the underlying facility selection process. Publications that examine design optimization and cost estimating for specific technologies and/or projects are abundant (see for example Brooks and Carroll (1994), Zimmer (1994), and Stokes et al. (1996)). The gap we are referring to is in the area of aggregated analysis across projects and operators; the most recent published study known to the authors is Karlik (1991). Sample selection problems in statistics and regression analysis occur when the sampling is not random. In this case study, the samples of spars and TLPs are the result of a selection process assumed to be based on profit maximization. Therefore, the observations cannot be construed as a random sample and additional computations are required to correct for this feature of the data.
Reservoir Study Considering a TLP and Dry-Completion Development for a Deep Offshore Heavy Oil Field
The continuous search for new oil resources is driving the major companies to start considering the development of offshore heavy oils (OHO) fields. Although not as heavy as inland heavy oil fields, where thermal methods are usually employed, development of these fields imposes extra technical difficulties as waterflooding might be the only feasible economical alternative for increasing recoveries. The process is used in association with long horizontal production and injection wells carefully placed in order to take advantage of the geological setting. OHO projects may face several production related problems during their lifetime, such as early water breakthrough and increasingly fast water rates. Other problems may also arise: strong water-oil emulsions, flow assurance problems related to hydrates, poor gas lift efficiency opposed to a less reliable satellite wells electrical submersible pumps (ESP) technology. Moreover, most offshore developments are based on FPSO production systems and satellite wells where one has limited (and highly expensive) well access. The reservoir study described in this paper quantifies the worth of reservoir management actions that would be feasible in a TLP production system equipped with drilling and workover capabilities. This work focuses on the optimization of a development plan using different technologies such as side tracking existing wells, multilateral wells, ESPs, etc. A thorough discussion on the impact of operational efficiency factors (downtime factors) is also offered. The methodology described in this paper is applied to a full scale reservoir flow model that was continually history matched with oil/gas/water flow rates and measured downhole pressure data, gathered by a one well pilot production system for over a year period. The results of the study are interpreted in terms of the scenarios more suitable for TLP developments. The benefits are directly related to the complexity of the reservoir geology which, in turn, governs the possibility of field development by phases. Other aspects such as the existence of pipeline infrastructure nearby the field and use of innovative artificial lift systems may either help or impair the use of dry completion wells concept. Although the present study reveals the FPSO option as the best economical alternative, for the particular field investigated, this work sets a standard for future OHO development studies. Introduction Exploration efforts offshore Brazil have been indicating important heavy oil discoveries in reservoirs that are often located in deep waters. In this paper, the terminology "offshore heavy oil" is applicable to oils with an API below 20, with viscosities at reservoirs conditions over 10 cp and surface viscosities over 500 cp. The economic exploitation of these reservoirs presents a series of technological (and economic) challenges that are very properly addressed in the literature (1, 2).Deepwater fields are often developed through floating systems using FPSO or SS type platforms, with subsea well completion (satellite wells).However, this development concept hinders well workovers due to high costs and limited availability of workover rigs with dynamic positioning facilities.This is results in fewer possibilities for an active management of producing reservoirs. A flexible and timely reservoir management is particularly important for heavy oil reservoirs, as they present early water production problems, with lower recovery rates and associated flow assurance problems.
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