Statoil drilling and well learning curves, experience and theory : is there a learning curve from drilling the first well with a new rig and onwards?
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This thesis is performed for Statoil in Statoil premises in Stavanger. Statoil is a large offshore and subsea oil and gas operator in Norway, with increasing activity internationally, both offshore as well as on land. The thesis will look at Statoil’s experience data, mainly in Norway, and compare these with established learning curve theory in the drilling and well business, in order to develop an updated method for learning curves in the time estimation process in the company. Time and cost estimation is tightly linked and this thesis’ main contribution will be to the time aspect. The time and cost aspects of drilling and well operations have gained increased attention during recent years, with strongly escalating cost, and the thesis is a part of the initiative to improve the estimation process. There is in the order of 100 wells drilled and completed each year in Norway, 70% of these by Statoil. Statoil is also involved in operations internationally, in example South America, Iraq, West Africa, Gulf of Mexico and Shale Gas in USA. The cost of these activities is considerable, hence the importance to maintain effectiveness, and even better; “to improve performance both short term and over time”. This report is based on data acquired from 97 drilling facilities and 3267 wells. Ideally the first wells should have a steep learning curve, towards the technical limit, where the minimum time usage is reached for the lifetime of the investment. Earlier findings indicate that there are different opinions and understandings within Statoil of what learning curves are, as well as what they should be used for. The main application of learning curves is time estimation. To verify or improve the current learning curve used in drilling, Completion and Well Operations today, DBR data have been used for learning curve analysis. The analysis has been split into different rig types. A Learning Curve Analysis tool has been developed in Excel, where DBR data is imported and analyzed for the proposed new learning curve parameters and estimation process. The new learning curve parameters also include a delay parameter in addition to the standard Brett Millheim parameters. Results indicate a poor level of learning measured by the Brett Millheim C2 learning coefficient and alfa value. The alfa value tells how much additional time is added to the technical limit, in relative terms. There is also higher spread in the semi submersible results compared to the others rig types. The larger spread gives very high max values for the lowest efficiency wells. This is suggested to be an area of increased focus, both because of the performance numbers, but also because a very large portion of Statoil’s wells are drilled with semi submersible’s, and last because the rig rates usually are the highest for these rigs. And hence even small improvements can give large improvements. Based on the results from the analysis and specific findings, some rigs were selected for a more in-depth analysis. Good examples with good field related learning have been found when using an old semisubmersible drilling rig for a series of comparable wells. A brand new rig drilling with its first wells is also presented. Another question is also how improved and faster learning can add the most value to the company? This is a question with many different opinions, however high involvement, in Statoil today. This thesis would like to promote the “knowledge and value approach”, meaning that increased understanding of time usage and learning processes can become a useful (and powerful) tool to visualize where on the learning curve a specific well are located, in order to be able and select reference wells more knowledgeable. And in operation, help to see in what direction the efficiency are going. It is suggested that the personnel III influencing the “underlying large cost drivers” such as method selection, procedures, etc. should have easy access to learning curve competency and information. The potential upside from an increased focus on learning curves as a management and operation tool is considerable. The economical significance is in the in the order of being able to justify projects or not. Numbers like 25, 35 and even 50% and higher reduction in time usage is mentioned in literature, if you manage to raise the learning level towards a normal and excellent level. It is suggested to: Improve the data quality in DBR for key parameters used in LCA analysis Include Learning curve analysis in the Drilling and Well Estimator (DWE) Perform Learning Curve Analysis (LCA) follow-up on completion and Well operations Continue and expand relationship with University of Stavanger (UiS) and Petroleum and Asset Management departments (Aadnøy and Liyanage) Platforms where the drilling facilities have been upgraded to meet new HSE standards, etc. have been looked upon in order to see if possible to measure performance before and after with respect to learning. These upgrades usually takes place late in the field life, and the drilling targets before and after upgrade are very scattered as well as very few completely new wells drilled (one of the analysis criteria). Instead one finds a lot of different types of sidetrack’s and some extended reach wells making it difficult to compare directly before and after upgrade. It is suggested to go one step more into detail in the dataset in order to compare time for parts of the drilling process, similar to what has been done in the pilot study of this thesis. It is also suggested to include the LCA analysis tool into the next version of the DWE estimator, where a natural framework is already built-in. An LCA analysis ability applied here will improve the understanding for what learning curves represent in practice, and what factors have an influence. In the data made available there has been found for the existence of a “forgetting factor”. This is further described in this report. There has, also, been found support in the data to claim “learning delay” in some types of operations. The thesis work has included the following sub-processes: Literature study, to find out about and verify the industry standards. Set-up measurement parameters. Develop tools and procedures for data extraction from the drilling reporting database (DBR) and Learning Curve Analysis (LCA). Perform in depth analysis of 97 drilling facilities and 3267 (qualified) wells from the DBR data base system. A total of 5-10 000manual data steps have been performed Establish guidelines for dealing with uncertainties and finding the limitations in the dataset. Main focus has been startup of drilling facilities on fixed platforms, and the first (5) wells. Assessment of whether the current Statoil time estimation learning curve application model is OK, or if (the) industry standard give a better description Analysis of whether we see a learning curve at all. Can we better predict where and when we will see, and where we will not see a learning curve? Can project learning curves be estimated in advance? How we can improve learning speed has also developed as the work progressed, and maybe a separate thesis is needed to properly answer such challenges. Conclude with several specific focus areas for future work, both internally, and together with an academically institution. As well as some findings, and indications that can be useful in the ongoing work within performance measurement and management towards an even more competitive Statoil in the future.
Master's thesis in Offshore technology
PublisherUniversity of Stavanger, Norway
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