How to Improve Rate of Penetration in Field Operations
- M.J. Fear (BP Exploration Co. Ltd.)
- Document ID
- Society of Petroleum Engineers
- SPE Drilling & Completion
- Publication Date
- March 1999
- Document Type
- Journal Paper
- 42 - 49
- 1999. Society of Petroleum Engineers
- 4.1.5 Processing Equipment, 1.12.3 Mud logging / Surface Measurements, 5.3.4 Integration of geomechanics in models, 4.1.6 Compressors, Engines and Turbines, 1.6 Drilling Operations, 1.2.3 Rock properties, 4.3.1 Hydrates, 1.5.4 Bit hydraulics, 4.3.4 Scale, 1.12.6 Drilling Data Management and Standards, 1.6.1 Drilling Operation Management, 1.11.2 Drilling Fluid Selection and Formulation (Chemistry, Properties), 1.5 Drill Bits, 5.6.1 Open hole/cased hole log analysis, 1.11 Drilling Fluids and Materials, 1.5.1 Bit Design, 1.10.1 Drill string components and drilling tools (tubulars, jars, subs, stabilisers, reamers, etc), 4.1.2 Separation and Treating
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A method has been developed to identify which factors are controlling rate of penetration (ROP) in a particular group of bit runs. The method uses foot-based mud logging data, geological information, and drill bit characteristics, to produce numerical correlations between ROP and applied drilling parameters or other attributes of drilling conditions. These correlations are then used to generate recommendations for maximizing ROP in drilling operations.
Time spent drilling ahead is usually a significant portion of total well cost. In typical wells drilled by BP Exploration (BPX), this "rotating time" usually accounts for 10% to 30% of well cost. This means that the penetration rate achieved by the drill bit has considerable significance for drilling cost reduction.
Despite this, the bit/rock interaction, and the ROP that results, is not well understood in detail. Other drilling phenomena, such as torque/drag modeling or directional behavior, are typically managed with the aid of validated, quantitative models. Such tools are conspicuously absent from planning or analysis of bit performance.
Within drilling operations, uncertainty over expected ROP clouds decisions on which bit types and downhole tools to select to maximize ROP. That intuition is prominent in these decisions demonstrates the inability of drilling data or predictive models to provide anything more substantial.
Against this background of commercial significance and technical difficulty, BPX is developing engineering methods to raise ROP. This paper describes one such method, and its application and benefits to one drilling operation.
Background to the Method
Table 1 lists factors which are proposed to affect ROP.1-18 The number of factors hints at the complexity of the bit/rock interaction, something which is compounded by interdependence and nonlinearity in some of these effects.6,7,11,15,18
Laboratory studies and modeling are however unraveling this complexity. For example, how ROP responds to changes in drilling parameters has been shown to depend strongly on rock properties. In permeable rocks for example, overbalance pressure influences ROP,1 giving way to a dependence on bottomhole pressure as permeability decreases.2-4 Overbalance pressure effects however are subject to dynamic influences, either via filtration effects on pore pressure in the bit/rock interaction zone,5 or via stress effects on pore pressure around the wellbore.6 Bit cleaning effects while drilling hydratable formations in water base drilling fluids ("muds") may also override the effects of mechanical drilling parameters, so that rock mineralogy and mud chemistry are obviously significant factors.7,8 These cleaning effects are however themselves influenced by bit design,9 and jet nozzle arrangement.8,10 In summary, rock properties that influence ROP include at least mineralogy, strength, density, porosity, and permeability.2,3,5,8,9,11,14 Interdependence between mechanical and hydraulic drilling parameter effects may also be significant, meaning for example that the response of ROP to weight on bit (WOB), rotary speed and flow rate can depend on absolute values of these parameters.7 Bit design effects are also not simple; differences in bit design effects on ROP with polycrystalline diamond compact (PDC) bits appear only to become significant when bit cleaning problems occur, or when cutters become worn.9 With roller cone bits, varying jet nozzle arrangement may or may not affect ROP, depending on which of bit or bottomhole cleaning is deficient.8,12 Finally, complexity is increased by errors and inconsistencies in drilling data, meaning that correlations with ROP may be masked without extensive data treatment.13 This latter point may explain why, despite publication of a number of analytically derived ROP models,19-22 none has yet become established as an operational tool.
The complexity of the bit/rock interaction, and the difficulties with implementation of analytical models, have encouraged BPX to adopt an empirical approach to optimization of ROP in drilling operations. This method follows that approach.
Emphasis has also been placed in this work on understanding the effects of controllable variables, i.e., those that can be readily changed to optimize ROP. Other environmental effects are however incorporated into another ROP modeling technique developed by BPX and described elsewhere.13
Description of the Method
The objective of this method is to quantify the effects of operationally controllable variables on ROP. To reveal the effects of these variables, data sets must be constructed so as to minimize variation in environmental conditions. The first step is therefore to select a group of bit runs made with the same bit size, through similar formations. Next, intervals of consistent lithology are identified, with a preference for formations exhibiting lateral homogeneity. Formations such as shale and limestone are, in general, more suitable than variable lithologies such as sandstone. Rock property logs can of course be used to verify comparability. Depending on the objectives of each specific analysis, further sorting can be made to separate bit runs made in different mud types, with different classes of bit, or to separate intervals drilled with sharp bits versus those in worn condition. Each step helps to further expose the effects on ROP of bit design and mechanical or hydraulic drilling parameters.
Once intervals have been selected and sorted, numerical averages of the variables of interest are obtained. This is critical because many sources of error exist in drilling parameter measurements and, pending improvement in data quality, averaging to raise sample size is the most obvious method to minimize error effects.13
Fig. 1 shows a log where data have been extracted and averaged from an interval of shale early in the bit run, prior to a drop in ROP related to bit wear in a sandstone. This process would then be repeated for other bit runs made through the same stratigraphic interval, yielding a data set suitable for analysis.
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