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We collect a lot of data in order to decipher and understand our reservoirs, whether from logs and tests in exploration and appraisal wells to data regarding multiple parameters from production wells. There is often a trade-off between data gathering and time/cost to gather the data, but there is usually no compromising the analysis of such hard-earned data. What we often find however is that people fail to make the most of is the large amount of data in the public domain. This is often available free and, whilst often in raw form, rather than analysed, can be analysed and used to extract meaningful data. In this report we interrogate several public data sources and give several examples of analysis undertaken to reveal the regional scale trends for use in revealing unconventional exploration potential to production performance characteristics:
CSG and water production data from Queensland; Unconventional resource estimate from SA, Queensland and the Northern Territory; Gas production and demand forecasts for the Australian East coast and how these can be linked with the respource estimates for the region; Gas and oil production data from South Australia; and Reserve, resource and production estimates from Wertern Australia.
CSG and water production data from Queensland;
Unconventional resource estimate from SA, Queensland and the Northern Territory;
Gas production and demand forecasts for the Australian East coast and how these can be linked with the respource estimates for the region;
Gas and oil production data from South Australia; and
Reserve, resource and production estimates from Wertern Australia.
The methods used generally require only familiar tools such as Excel and ArcMap GIS spatial analysis.
The results can be tailored according to the analysis required but may include any number of parameters, e.g. peak gas or water rate, ultimate gas recovery, gas in place per unit area, and regional trends in such parameters.
We trust that this review will re-awaken analysts to the various public domain data that are available, which, although not always perfect, may provide valuable insights.
Boulders are known to be present within foundation zone depths at some Atlantic East Coast wind energy development areas, which can make it difficult to level a piled jacket or template and can lead to the progressive collapse of the pile toe, causing premature refusal during pile driving. Although detection and avoidance are preferred over mitigation, numerical analysis methods are available to assess the risk of pile refusal, which allows for informed decisions on whether avoidance is required and what types of mitigation to consider during construction. Detailed numerical evaluation (using one-dimensional wave equation analyses and two-and three-dimensional finite difference and finite element modeling) was performed to develop a better understanding of stresses in the pile during driving. The numerical modeling evaluated the effect of strength, thickness, inclination, shoe length, wall thickness, and lateral continuity on pile stresses. A three-dimensional model of the pile and driving shoe subjected to stress-time histories was used to evaluate the stresses at the pile toe and at the transition from the pile to the driving shoe. Example results are presented to illustrate failure mechanisms of hard layers that include boulders, and high-level guidance is provided on operational sequences and potential contingency measures.
This paper presents case studies on reservoir and well management of two laterally and vertically compartmentalized Western Australian Triassic gas condensate reservoirs, developed by five multi-zone "smart" wells with sand control, tied back to an offshore platform via a subsea network. In managing assets with such complexity, it is imperative to understand reservoir performance on a zone-by-zone basis. Quantifying performance allows management of flux through downhole sand control systems and optimisation of offtake strategy. The majority of the material published to date on "smart" wells has been focused on completion design optimisation and minimisation of unwanted oil/water production. There are few existing articles about production and reservoir optimisation of high rate gas wells requiring flux management.
This paper showcases how remotely-operated selective completions ("smart" wells with permanent downhole gauges for each completion coupled with subsea flow meters for each well) have been instrumental in facilitating prompt analysis of zonal reservoir performance and thus in yielding insights into reservoir connectivity and allowing optimisation of zonal contributions. Various case studies will be presented showing how reservoir surveillance data is acquired and interpreted to optimize well zone-by-zone production and to manage flux limits on each producing zone. These case studies will include manipulation of downhole valves to provide information for established techniques such as interference testing and P/Z analysis.
Data acquisition and interpretation challenges are highlighted along with fit-for-purpose solutions developed to overcome those challenges.
The insights presented could facilitate better planning of similar systems in the future.
On-bottom stability of subsea pipelines has been the focus of substantial research in recent years with particular emphasis on the topic of pipe-soil interaction. Numerous models have been developed to predict the pipe-soil interaction behavior under combined vertical and horizontal loading scenarios. The Verley and Sotberg energy based soil resistance model for silica sand is among these models. The model is recommended by Det Norske Veritas (DNV) and widely accepted and used by subsea pipeline design industry to model the pipe-soil interaction on silica sand soils. Calcareous sand soils are found in many of the world’s offshore hydrocarbon development regions including offshore of Western Australia. The engineering characteristics of the calcareous sand are different from those of the typical silica sand soils. As such, Verley and Sotberg parameters for silica sand soils are not suitable for calcareous sand soils. This paper presents a calibration of the Verley and Sotberg silica sand soil resistance model for calcareous sand soil conditions using the results of a set of centrifuge tests of a pipe model on calcareous sand soil.
Copyright 2012, Offshore Technology Conference paper was prepared for presentation at the Offshore Technology Conference held in Houston, Texas, USA, 30 April-3 May 2012.This paper was selected for presentation by an OTC program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not beenThis reviewed by the Offshore Technology Conference and are subject to correction by the author(s). The material does not necessarily reflect any position of the Offshore Technology Conference, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Offshore Technology Conference is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of OTC copyright. Abstract Installation of offshore platforms in carbonate soils is challenging, as evidenced by numerous incidents and accidents.
Probabilistic aggregation and dependency estimation are essential in portfolio methods, production forecasting, and resource estimation. The use of arithmetic addition understates the true value of the resource estimates within a portfolio of fields. Potentially, this could result in deferral of a project, or loss of lucrative business and commercial opportunities, such as project investment, facility-sizing decisions, or incremental gas-supply commitments.
A statistically robust method for aggregation of resource estimates that appropriately uses expert opinion is presented in this paper. Using two integrated-project examples, this paper introduces new methods for (1) probabilistic aggregation of the resource estimates for multiple fields and (2) estimating a measure of dependency between the resource estimates of individual fields.
The new analytical method for probabilistic aggregation is based on multivariate skew-normal (MSN) distributions, which can model a wide range of skewness through a shape parameter and are used heavily in financial and actuarial applications.
In studies of the fields in which the multiple-realizations approach is used as a basis for the uncertainty framework, tornado diagrams are generated routinely to describe the dependence of the field resources on reservoir parameters. The improved method for evaluating measures of dependency between the resource estimates within a portfolio of fields uses these tornado diagrams as a basis. Incorporating the expertise and knowledge of geologists and petroleum engineers is a critical element of the method.
These methods for probabilistic aggregation and estimating dependencies were developed within the context of the oil industry, but their use is not limited to the oil industry. They are general and can be used in other probabilistic-aggregation problems. Application of these techniques requires limited time and effort, compared to individual-field studies, and can have a profound impact on the uncertainty range of the total resources for the portfolio of fields.
J4 project was initially an 8 well small field development. Initial well designs were of 2 dimensional trajectories, a total depth of 7000 feet and a tangent angle not exceeding 60 degrees. Average well P50 drilling authorization for expenditure was calculated at 25 days. By definition of small field, the economics of the project was under management scrutiny with positive performance expected. After drilling the first J4 well and exceeding the AFE by 25% and the eventual loss of the platform slot while drilling the second well, it was apparent that a complete step change during planning and execution was required for the success of the project.
For effective step change, the recently enhanced Performance Improvement Process (PIP) was used. PIP encompasses other performance enhancement methods providing the necessary tools to undertake problem solving in a methodical manner. Additionally, for PIP to be effective it was crucial to harness the experience, knowledge and uninterrupted contribution of the well-construction team. PIP was designed to complement the Core Workflow Process, a systematic method for well planning, well design, and well execution. Besides PIP measurement, the team would investigate the rate of return on investment made in the performance improvement process The J4 team was keen to highlight what level of investment in performance improvement was justified in the tight budgets of a small field development.
As time was limited and the third well pending an assessment and rethink of the remaining wells by way of a structured fast track approach was essential. This would be the first time PIP would be applied to accelerate change to a project in Petronas Carigali SDN.BHD (PCSB). PIP provided the structure and format to enable theteam to embark on a comprehensive examination of the geological data, update the geological model, and re-design the projects remaining wells and trajectories through to their successful execution.
This paper will discuss the PIP tools, methods and practices utilized by the well construction team to successfully fast track and improve drilling performance while returning a positive return on investment for the changes made.
This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Predicting total system deliverability is important as Contracts are often long-term, prescribe gas quality constraints and set limits for the concentration of impurities; - Complex ownership structures and operating arrangements, due to high capital intensity. These challenges establish the need for forecasting tools that cover the full supply chain of a project, from the reservoir to the physical point of sale and the sales contracts. Woodside's bespoke Database Integrated Modelling Environment (DIME) has been built specifically for forecasting and optimising complex LNG projects producing from a diversity of fields with significantly different condensate and LPG yields. The tool provides fast, accurate forecasts for: - sales products - LNG, domestic gas, chemical feedstock (e.g. Because DIME enables modelling from the reservoirs to the plants and sales delivery point, it forecasts sales products and therefore directly provides a forecast for revenue. The model is fully compositional and pressure-sensitive, and designed for system optimisation. This paper describes the tool's functionality, focusing on the technical challenges of forecasting and optimising the complex production system of the North West Shelf project. DIME integrates well with other tools and supports a range of technical, business and commercial processes. It has been used by Woodside over the last 10 years, and has established optimum project timings, sizing compression requirements and identified system constraints. Introduction - the Forecasting Challenge The forecasting challenge for a bundle of gas fields developed as a single project, incorporates all the complexity of the development of a single gas fields, compounded by the large number of combinations to connect the fields, to schedule the fields and to capture synergies between the development of the fields.
Addis, Michael Anthony (Shell International) | Gunningham, Michael Caspar (Sakhalin Energy Investment Company Ltd) | Brassart, Philippe Charles (Shell) | Webers, Jeroen (Sakhalin Energy Investment Company Ltd) | Subhi, H. (SEIC) | Hother, John Anthony (Proneta Ltd.)
Copyright 2008, Society of Petroleum Engineers This paper was prepared for presentation at the 2008 SPE Annual Technical Conference and Exhibition held in Denver, Colorado, USA, 21-24 September 2008. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract Sand Quantification involves predicting the volumes of sand which can be produced at the sandface completion and transported to the surface facilities for different operational scenarios. Sand quantification estimation is still novel in the industry, and this paper describes its application in completion selection and design, facilities design and operation, and facilities risk evaluation, with reference to a high rate gas field development. The estimation of sand production volumes for openhole and cased and perforated completions is presented for the high rate gas wells, along with the workflow used for the selection and optimisation of the completion design, based on these estimates. The optimum completion aims to delay the onset of sand to surface for the first 18 years of production, whilst maintaining high gas productivity ( 300mmscf/d/well). The selection of contingency sandface completions is also discussed along with mitigation measures in the event of unexpected sand production. The impact of the sand quantification on surface facilities design is discussed based on a probabilistic approach, along with the operational procedures identified to manage this sand.