Dynamic under balance (DUB) pressure is being used by operators to improve completion efficiency and minimize formation damage. An understanding of the pressures generated when perforating allows the prediction of the level of pressure differential required to overcome the surge pressure generated, leaving a clean connection between the wellbore and the reservoir. Results reported by different operators indicate success with the DUB technique; certain criteria have been developed for the determination of the optimum conditions for its use. From the published information, type of reservoir fluid, interval length and formation mechanical properties are some of the main considerations for a successful design and implementation of the technique. Talisman Energy UK has used DUB for perforating several wells in the North Sea. A detailed job design process was implemented starting with the need to characterize the level of formation damage that existed in the wells. Three (3) main damaging mechanisms were quantified; damage resulting from fluid and solids invasion into the reservoir matrix, mechanical damage generated during drilling operations and actual damage caused during the perforation process. Perforation programs were developed and executed based on a clear understanding of the rock mechanical response to the DUB generated by the gun and taking into consideration perforating debris and fluid inflow required to clean the tunnels. The results from this work indicate that current design criterion for the selection of the optimum DUB pressure is limited and it is not clear about the physics and hydrodynamics of the clean up process.
Despite continued advances in sand production prediction software, numerical modelling does not always agree with observed sandface behaviour once a well is flowing. Where core is available, computer models should be calibrated to laboratory determined rock strengths, measured on plugs cut from the core. The number of core plugs is often restricted due to time limitations and availability of good quality core. These may not be fully representative of the entire reservoir section due to complex variations in rock composition. Sandstone failure and the onset of sand production can be more accurately predicted by integrating log derived rock strength, calibrated to rock mechanics laboratory test data, with petrographic observations made on the core; i.e. combining quantitative and qualitative methodologies. Such observations typically include mineralogy, rock texture, structure and reservoir quality. Laboratory numerical data are discrete, whereas log and observational data are continuous, and these do not always readily relate. This can make the integration of laboratory-to-field data a difficult process. In an attempt to more closely relate quantitative and qualitative core data, a Schmidt impact testing hammer has been used to make hardness measurements along an entire core. The Schmidt hammer was originally designed for the non-destructive testing of concrete hardness in the civil engineering industry and was later adopted to estimate rock strength in mining, quarrying and tunnelling applications. The Schmidt hammer allows very rapid (and inexpensive) core testing, and has the added advantage of being non-destructive. Rock mechanics parameters can be determined in dense arrays that reflect the real inherent heterogeneity of the core. Use of the Schmidt hammer has enabled extrapolation of laboratory determined rock strength across the entire core and has facilitated direct correlation of mineralogy and grain texture to sandstone failure and yield. Characterisation of overlying claystones has also provided essential data for the determination of stress shadowing effects. Further development and refinement of this technique should provide an additional useful tool in reservoir and overburden characterisation and predicting sand production. The technique does not require specialist rock mechanics knowledge and can be applied at the well site or core viewing room. Useful data can be obtained from core material ranging from fresh state to old archived material. The speed and practicality of the Schmidt hammer as a rock strength characterisation tool is seen to have great potential. Introduction Sanding prediction work often relies on numerical analysis (quantitative) without regard to reservoir quality, rock texture and micro-structure from core observations and petrology studies (qualitative). The two methods of approach compliment each other, and when combined, greatly enhance strength characterisation of sedimentary rocks. In practice however, the integration of numerical and observational data is often fraught with difficulties and pitfalls. The unconfined (or uniaxial) compressive strength (UCS) is the most often quoted rock strength parameter. It is also the most readily understood sanding indicator by non-specialists in the field of rock mechanics. The UCS test has many disadvantages, but nevertheless has widespread and valuable application to wellbore stability and sand prediction. If UCS data are not available, many process steps are required to calculate an approximated UCS (Fig. 1). Laboratory UCS data can be plotted on a core log for the correlation of rock strength to petrological features. However, in heterogeneous reservoirs of complex sedimentology and mineralogy, the often-infrequent sample interval of UCS data makes accurate correlation and extrapolation difficult.
Wellbore clean up is a critical component of any new well construction. Failure to adequately clean the wellbore can cause major difficulties in running the completion resulting in large amounts of NPT with all the associated costs. Conversely, performing over-complicated clean ups to ensure success adds additional risks and unnecessary extra cost. This paper examines an operator's experience with wellbore clean ups over a wide range of assets in the UK and Norwegian sectors of the North Sea. The operator wished to reduce the time taken by developing best practice guidelines. This would provide a common approach to wellbore clean up operations over all assets. Using reports from 19 offshore wells completed between 2002 and 2004, the time taken during clean up operations was split between nine categories:Running ToolsChemical Clean up / DisplacementPit CleaningWaiting on EquipmentWaiting on WeatherPressure TestingNon clean up OpsSafety EventsWaiting on Permits This allowed the causes of extended time to be identified and pinpointed the areas where most time could potentially be saved. Some of these were:Clean up tool failuresOver-complicated tool-stringsBecoming stuck due to over ambitious toolstringsRig equipment failuresIncomplete tools being sent offshoreRepeating of chemical clean up due to wellbore filters being full of mud/debrisDelays while waiting for pit cleaning to be completedLessons not being learned from other assets These and other causes were further examined and discussed by operator staff in a workshop and vendor interviews. Guidelines were then developed by combining existing good practices from different assets and other industry experience. Background Talisman Energy is one of the largest oil and gas operators in the UK sector of the North Sea. At the time of this study, Talisman operates a wide ranging portfolio of assets in the UK and Norway comprising of platforms, FPSOs and many Subsea developments. Talisman has acquired all of these assets from other operators, normally retaining the staff and, to an extent, the working practices of the previous operator. As a result, there can be differing ‘best practice’ approaches between personnel working on different assets. For this reason as well as reviewing benchmarking data, a study was initiated with the aim of developing in-house wellbore clean up best practices to allow a common, efficient approach across all assets and operations. Benefits of Guidelines Will provide uniform approach across all assets. Lessons learned across assets are captured to ensure continuous improvement. Aid development of less experienced Engineers. Will reduce overall time of clean ups through consistent best practice use. Reduced time = Reduced costs
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