Use of effective sand-control practices has sustained oil and gas production from wells that would otherwise have shut-in. However, a large number of gravel-packs fail early after installation necessitating expensive well intervention. A basic prerequisite to effective control among others is a good gravel-pack design and execution1. This includes obtaining a representative sample of the formation sand, analysing the grain-size distribution and selecting an optimum gravel-size. Gravel-size selection is carried out in relation to formation grain-size to control formation sand movement and using the optimum screen slot to retain the gravel. However, core samples are generally not taken in all wells in a field. In the absence of specific well information, it has become accepted practice to use offset-well data for designs creating a potential for ineffective gravel-packs. This paper discusses the application of neural networks to obtain real-time, well specific, grain-size distributions and how this input could be used to improve gravel-pack design and achieve optimum sand control. Neural networks have been applied with success to predict grain-size distributions from well logs. The availability of a continuous grain-size profile across an entire reservoir has facilitated comparison between the size of gravel on existing gravel-packs where offset-well information was used and gravel-size obtained based on neural network estimates. The results indicate significant differences. They further demonstrate that the use of estimates of grain-size distribution across the entire reservoir rather than offset well grain-size can lead to improved gravel pack performance and thus significant savings on life-cycle costs. Introduction For decades gravel packing has proven to be a succesfull technique for sand control. However, the method has been associated with a reduction in well productivity. The key to successful gravel packing involves selecting gravel of the proper size and quantity and placing the gravel without contamination at the proper location2. It is therefore evident that gravel pack impairement can result from a combination of these factors. One of the most compromised factors of all is the selection of proper gravel size. Early literature on use of formation grain-size for gravel pack design is based on the work of Coberly and Wagner (1938)3. Saucier (1974)4 published results of tests with physical models; he stated that ‘To apply the recommended design criteria, the important information required - knowledge of formation grain-size - is often the least available’. The reality of the unavailability of formation grain-size data is due to the fact that coring is expensive and therefore seldom carried out on development wells. Gravel pack designs have had to be based on grain-size data obtained from offset wells rather than the well in question and so negating the effect of reservoir heterogeneity. In some cases designs are based on very scanty sieve data from the subject wells - in some cases as few as five formation samples over large reservoir sections are used. Poor data from sources such as bailers, sidewall samples or production samples have also been used. These practices create a potential for the gravel pack system so designed to be ineffective or fail in early life. In an earlier paper (SPE 56626)5 the authors showed that grain-size prediction is feasible from wireline logs using neural networks. Using this technique, a backpropagation neural network can be trained with available grain-size distribution and well logs in a field and used to characterize grain-size distribution in subsequent wells in that field. Thisin situ approach means the just-in-time availability of a continuous log of grain-size over an entire reservoir section.
More than 80% of oil and gas reservoirs especially in HP/HT wells and deep water environments of the Atlantic Margin are known to be highly unconsolidated requiring some form of advanced, complex well geometry sand control completions. A key factor in an optimum sand control completion is a thorough knowledge of formation sand profile, which is traditionally determined by sieve analysis of samples obtained from cores, production lines, etc. In complex wellbore geometries, the samples obtained through these traditional processes may be totally unreliable. In this paper, a neural network technique for predicting grain-size distribution from wireline logs is presented. The paper briefly introduces neural networks and discusses the implementation of the network and its application to Smart Well Sand Control Completions. The network is recommended for application for either real-time prediction (that is, while drilling) where input data may be fed directly to the neural network prediction model, or for prediction at office base depending on the requirements, urgency and purpose of the data. Benefits of the new model include real time prediction of formation grain-size and more reliable prediction of grain size in complex well geometries where the use of traditional sand sampling would be difficult. There is potential for application in the prediction of plugging tendency of screen systems and gravelpacks.
The use of gamma-ray log shapes to determine grain-size trends is commonplace despite obvious limitations (Rider, 1990, Hurst, 1990). In a recent development, this implied qualitative relationship between the gamma-ray response and grain-size was exploited in developing a quantitative means of predicting grain-size from well logs (Oyeneyin and Faga, 1999) using neural networks. However, for this in situ prediction methodology to succeed the relationship between gamma-ray log values and clay content and between clay content and grain-size needs to be consistent within a range that will not negate the generalisation capabilities of a Back propagation-Neural-Network. One of the main factors affecting the natural variability that exists in these relationships and causes reduction in porosity and permeability is diagenesis. The others include compositional and textural factors (Rider, 1990). This paper presents the results and recommendations of a study carried out to determine the effects of diagenesis on the accuracy of non-linear grain-size modelling in sandstones. The results of the study indicate that grain-size modelling in diagenetically modified formations is feasible. It characterises the effects of cemented zones. The study further concludes that cross-depositional environment prediction of grain-size in the case of the Brent and Statfjord formations generates representative grain-size trends and values. Introduction The basis for grain-size modelling using well logs is the relation between grain-size and gamma ray log measurement, the relation between compositional and textural changes and the porosity and resistivity logs and the desirable non-linear capabilities of neural networks. In other applications, neural networks have been used for permeability prediction, porosity prediction, and lithology identification and found to provide basis for detailed inter-well correlations of diagenetic rock-types and their lateral permeability trends. However, no previous attempt has been made to understand the nature of the influence of diagenesis on neural network predictions of grain-size. In this paper, fundamental questions regarding the feasibility of grain-size prediction in the presence of diagenetic phenomena have been addressed by carrying out analysis with data from diagenetically modified Brent and Statfjord formations. Effects of depositional environment and diagenesis. Clay mineral diagenesis in sandsotones affects primary properties. This can include effects on the size and shape of particles, mineralogical composition, porosity, permeability and sedimentary structure. These changes increase the complexity of the clay to grain-size relationship. For instance grains may increase in size by recrystallization or decrease because of leaching and conversion of K-feldspar into illite can create an increase in the radioactivity present in the clay-sized fraction. Some diagenetic clay material may be less radioactive than some detrital ones (Rider, 1990). The distribution of authigenic clays in reservoir sandstone can be quite variable and since small-scale changes in diagenetic fabric can cause large fluctuations in porosity and permeability (Grigsby et al, 1996) such variations are imprinted on well logs in various forms. Diagenectic influence on well logs. In a study to determine if and how sedimentary fabric and mineralogy affect the responses of geophysical logs, Siron and Segall (1997) noted in part regarding the South Carolina Coastal Plain thatIn surficial fluvially derived cobbles the high clay/sand ratios dominated by kaolinite produced high resistivity signals and low gamma ray values.High clay/sand ratios within some racies reduced effective porosity and permeability and contributed to high-resistivity readings.Calcaleous Eocene sediments are characterised by highly variable signals on the gamma ray. SP and resistivity logs.Phosphatic material, smectite and disseminated organic matter produce very high gamma ray values; corresponding low SP readings are a function of low macro- and microscale porosity resulting from the fine grain size and authigenic pore-filling cement. Diagenectic influence on well logs. In a study to determine if and how sedimentary fabric and mineralogy affect the responses of geophysical logs, Siron and Segall (1997) noted in part regarding the South Carolina Coastal Plain thatIn surficial fluvially derived cobbles the high clay/sand ratios dominated by kaolinite produced high resistivity signals and low gamma ray values.High clay/sand ratios within some racies reduced effective porosity and permeability and contributed to high-resistivity readings.Calcaleous Eocene sediments are characterised by highly variable signals on the gamma ray. SP and resistivity logs.Phosphatic material, smectite and disseminated organic matter produce very high gamma ray values; corresponding low SP readings are a function of low macro- and microscale porosity resulting from the fine grain size and authigenic pore-filling cement.
fax 01-972-952-9435. AbstractThe 'lean and green' operating philosophy adopted for a field development plan in a Nigeria major E & P company in 1996 focuses on spending less and leaving the environment with minimal impact. Previously, the company cuts an average of 0.7 hectares of vegetation per well in the swamp to create drilling locations. Entry to a pre -installed multi well platform in a dredged drilling location by a specially built rig in October 1999 marked the inception, industry-wide, of platform drilling in the swamp .The successful deployment of this custom made rig to a six-well platform, each well spaced 2.5 meters (8 feet) apart, and the hooking up of the first well in the cluster Monday, 27 November 2000, ushered in the first oil from a multi-well platform operating in a concurrent drilling and production mode from an inland water location. This is indeed company and industry-wide achievement. Since then, five more wells have been drilled and completed from the same location, thereby saving over $2.0m from location preparation and avoiding multiple rig moves. The use of one single location to drill six wells significantly reduced the footprints on the environment in line with the company's mission in that respect. This paper presents the planning and execution of drilling, completion and production in a cluster location concurrently using the purpose-built rig during a recent campaign.
Several operators have recently launched a new industry-wide initiative on sand control reliability. The aim of the initiative is to gain a better understanding of Sand Control Completion (SCC) systems, equipment performance, and reliability in a variety of applications. It focuses on assisting the industry to improve SCC performance and service life through sharing of reliability and failure information, operational practices, and other pertinent data. One of the key challenges in this effort is how to achieve consistency in the data collected by several operators. This paper presents an approach to establish consistent practices for collecting, tracking and sharing SCC reliability and failure information. The approach is based on two key elements: (1) common data set; and (2) a standard nomenclature for coding SCC failure information. The general data set contains basic information on operating conditions, SCC systems and equipment, and the observed failures. While this data set is not overly detailed, in that the information is typically already collected by most operators and relatively easy to obtain, it is comprehensive enough so that meaningful analyses can be performed. The nomenclature standard builds on the International Standard IS0 14224 that stipulates broad definitions and failure attributes related to collection and exchange of reliability and maintenance data for equipment used in the petroleum industry.The paper also provides a review of past industry efforts to track SCC system reliability in terms of the types of data collected, and the main types of analyses performed with the data. Comments are included on difficult issues such as how to define failure of a sand control completion.It is hoped that the paper will encourage discussion on the topic, and help the industry share SCC reliability and failure data in a more consistent manner. The ultimate goals of this work are to assist the industry in improving SCC service life; improving the basis for selecting sand control systems and equipment; and better realizing the full potential of SCC technologies.
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