OTC MS. New Findings in Drilling and Wells using Big Data Analytics J. Johnston, CGG; A. Guichard, Teradata. Abstract

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1 OTC MS New Findings in Drilling and Wells using Big Data Analytics J. Johnston, CGG; A. Guichard, Teradata Copyright 2015, Offshore Technology Conference This paper was prepared for presentation at the Offshore Technology Conference held in Houston, Texas, USA, 4 7 May 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 been 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 In the current cost-saving and high-tech environment, this paper aims at demonstrating that significant business value can be derived from advanced information technology. The objective was indeed to identify and reduce risk in the Drilling and Wells domains using iterative, multi-disciplinary Big Data analytics and workflows. Examples of operational risk identified in this project include low borehole quality, poor wellbore stability, and stuck pipe. Subject-matter expertise and advanced analytical capabilities were assembled to mine and analyze large amounts of different data types across drilling parameters, petrophysics and well logs, and geological formation tops for a released data set of approximately 350 oil and gas wells in the UK North Sea. The data set contained information about a large geographical area, which conventional analysis techniques would find difficult, if not impossible, to handle and analyze in its entirety. Results of this study showed that iterative Big Data "discovery workflows" uncover hidden patterns and unknown correlations in the data and unexpected correlations across the data set are exhibited. It also confirmed the possibility to improve Drilling models using business analytics. In addition the correlations found allow predictive statistics to be computed. Finally advanced visualization capabilities provided an aid to interpret, understand, and make recommendations for Drilling plan and operations. This novel approach uncovered that patterns and correlations can be detected across a disparate data set, where data types are not traditionally linked, by integrating a large variety and complexity of data in one analytical environment. Furthermore the multi-domain analyses run during the study were all performed on-the-fly, without preconception or business requirements. As a final point Big Data Analytics can also be used as a Quality Control tool and will certainly be leveraged for further multi-variate analysis in Oil and Gas.

2 2 OTC MS Introduction The term Big Data Analytics is very current with applications across various industries. A common use in Retail is to identify consumer buying patterns from millions of transactions and records. The oil and gas industry has large data sets in a large variety of differing formats, such as seismic surveys, well logs, core and fluid analyses, drilling parameters, only to name a few. The interpretation and analysis of the diverse data typically follow standard well-known paths and workflows, e.g., seismic data is used to deliver surface and structures; well logs provide information on lithology, porosity and fluid properties amongst others. The main objective of this study was to evaluate if and how Big Data Analytics could be used with oil and gas data in an innovative manner to bring new information and hence new business value. The team composed of CGG and Teradata was convinced that a data-centric solution would allow analysis across multiple domains, formats and would also allow agile workflows without depending on specialized applications. In this study CGG provided the geoscience, well, and drilling data while Teradata enabled the iterative analytical approach to run complex data analyses. A conceptual diagram is shown in Figure 1 to highlight the logical and architectural aspects of the study. Figure 1: Diagram showing the logical and architectural aspects of the study For the purposes of this project well data was chosen because it has numerous differing data types from simple text files, through.pdf to specific formats such as.las storing well log data. There is also a vast amount of metadata associated with the well; these include (but are not limited to) location, lithostratigraphy, mud and bit types. The large variety in data types allows correlation analysis between them that would not ordinarily be considered. The choice was made of one specific problem for investigation using a limited, but not restricted data set. This allowed the analytic techniques to be tested paving the way for future detailed expansion of this pilot. The main question asked was Why do some boreholes increase or decrease from the nominal bit size? The answers given by the work described by this paper delivered some surprises.

3 OTC MS 3 Wells and borehole condition The selected business problem linked the disciplines of Drilling and Well Logging (including stratigraphic information), two domains not commonly associated though they are obviously connected. The placement of data in different silos for different groups of specialists to work on can create challenges in attempts to generate new and useful business insights. The sheer quantity of data generated in the industry also makes such data sharing difficult. Over three hundred wells from the UK Continental Shelf were used in this study. Information in digital format was: - Borehole logs, including Caliper - Drilling logs, Weight on Bit (WOB), Rate of Penetration (ROP), Torque - Deviation Additional metadata used was the location of the wells, lithological formations, and bit sizes. The location allowed a geospatial analysis to be performed while the stratigraphy gave the vertical dimension to be explored. Wellbores are drilled with a given bit size reducing with depth, for example 17.5 to to 8.5, with casing set at each intermediate step. Borehole logs give a measurement of the actual shape of the well. It is often far from the intended value. This can be due to a number of causes ranging from geological to mechanical. If the formation is made up of thin interbedded shales and sandstones either can collapse leaving no support for the other layers. Ultimately this creates a washout much larger than the bit size. The use of water-based mud in swelling shales makes the borehole diameter smaller than expected. All of these conditions cause significant problems for drilling and subsequent well logging (Figure 2). 100% 80% 60% 40% 20% 0% 34% 34% 27% 66% 66% 73% Figure 2: Well conditions per formation groups. Blue is good quality borehole condition, orange is poor quality. The figure is the percentage of logging points (several 100,000 s of points) in each formation. Total size of the dataset = 347 wells. 85 wells encountered the Chalk formation, 102 wells the Moray, and 83 wells the Cromer Knoll A separate but equally disruptive problem is that of borehole wall rugosity. This is where the diameter is not far from the bit size but exhibits small, often periodic, variations. These can render some measurements unusable. This phenomenon has not been investigated here in detail; it would simply involve adding some extra curves to the study for further results. 36% 64% Chalk Moray Cromer Knoll 6 Other Formations

4 4 OTC MS Drilling and the problems with bad hole One of the risks of drilling is for the drill string to become stuck. Among potential causes, one is enlarged or reduced borehole diameter. Washed-out holes can have pieces of rock falling on top of the drill bit trapping it. Smaller boreholes will trap the drill string due to the reduced diameter. If the drill pipe is stuck it has to be freed or fished, operations that take valuable rig time. If it has to be left in the hole the well has to be side-tracked, again adding time and expenses to the program. The driller monitors the progress of the operation using downhole measurements. If problem zones exist additional wiper trips may be needed to recondition the borehole in an attempt to avoid stuck pipe. In addition enlarged borehole diameters potentially cause problems for cementing the casings. The volume of cement is computed using the difference between the borehole volume and the casing volume. If the nominal bit size is used there will be less cement than required meaning an improperly cemented casing. Another problem is in the zones of large borehole; there the cement, even if the volumes are correctly computed, may not completely fill the hole leaving gaps or communication through which fluids may pass, a potentially deadly situation. Recording the borehole measurements is done either using Logging While Drilling (LWD) or on a wireline. The former is very useful and can save time however wireline is often needed for specialised tools that cannot be obtained with LWD. The condition of the borehole adversely affects both the acquisition of wireline data and its quality. The tools may also become stuck requiring a fishing job or even a side-track if this is not possible. Occasionally tools start to stick but manage to free themselves. However the borehole then needs an extra wiper trip before more logging runs can be made. The typical data summaries of a well do not indicate borehole problems. Traditional completion logs (an example of which is shown in Figure 3) have detailed geological information. The additional information added from an event log print-out shows there were some worrying problems. The information is available but it does require a lot of detective work to extract it. Figure 3: Example of a section of completion log, plus comments from the sequence of events and well log image showing problems encountered

5 OTC MS 5 The data quality is also a major issue as some key porosity measurements need a smooth borehole wall. In the example the borehole diameter shown by the caliper is well above the nominal value of This has caused the Neutron Porosity, Bulk Density and Acoustic Slowness to read incorrectly. The consequence is firstly the slowness will not match with the surface seismic, it shows additional reflectors where there are none. The other two measurements have been used to compute the Effective Porosity for input into the saturation computation. The porosity is much too high, the resulting calculation shows hydrocarbon where there is none (Figure 4). Figure 4: Example of a short section of wellbore. The red curve in the first track (left side) is the caliper. Track 2 has the Neutron Porosity and Bulk Density curves, Track 3 the slowness and Track 4 the interpretation result, which do not accurately indicate technical issues An initial result in the study (as seen in Figure 2) showed that about one-third of wells had bad hole conditions somewhere in the well and that some formations were more prone to the condition than others. Methodology The purpose of the study was to investigate if any drilling parameters could be linked to wellbore condition. As this involved different types of data, drilling and wireline plus metadata like stratigraphy and well location it was decided to leverage Big Data Analytics for the problem. Big Data Analytics has been described as the process of examining large amounts of disparate data types in an effort to uncover patterns, correlations and other useful information. The corollary not stated is that these trends will drive business decisions to change processes, enter new markets, save costs or generate more revenues. There is a vast number of techniques available to analyse the data statistically and analytically. Part of this study is to examine which of these have benefit to Oil and Gas.

6 6 OTC MS The tools used in this study allowed us to run the analytics on the fly, without any preconceptions or preloaded correlations. This gives the system unprecedented flexibility to investigate between the differing data types. The system also treats all data the same, so a yes/no input for deviation is equally valid with a log curve. The amounts of data available to the industry is vast, seismic is measured in petabytes while a modern well will generate well over a hundred different files. These could be core reports plus core photos, well logs in multiple formats, drilling, completion and geological reports, deviation surveys, borehole seismic surveys, well tests and so on. The file types are also very variable. Some data was in simple text files, some in industry standard formats such as SEGY. In this study we encountered files in.txt,.xls,.pdf,.las. All had to be read and loaded into the system. In the case we examined in this study the objective is to find ways of saving on drilling time and hence cost as well as enhancing safety and improving acquired data quality. To keep the problem manageable for this Pilot study the data was limited to Weight on Bit (WOB), Rate of Penetration (ROP), Torque and Caliper. The latter is the indicator of hole condition. This was normalised using the bit size to define a Differential Caliper (DCAL), the difference between the measured borehole diameter and the nominal value. The resulting value could be positive or negative, with the former state more common. Bad hole was defined as the DCAL either greater than Bit Size or less than Bit Size 0.2. These numbers were chosen qualitatively and are easily changeable. There is a number of other parameters and measurements that could potentially affect the borehole condition. Some examples include deviation and mud weight. Deviation is an obvious candidate in the search for bad hole causes. The actual deviation values were not used in the study but the wells were classified as deviated or vertical, with differences. Mud weight was also considered but not used in this Pilot; it remains a data type that will be employed in the future. To analyse all this disparate data we built a multi-domain analytical discovery environment and developed loading and Quality Control tools to enter the data. Data preparation was the most time-consuming part of the process with QC being essential. One recurring problem in handling borehole data is the name conventions used. An example is caliper logs variously labelled CAL, CALI, CAL1, CALR, CALS, C1 and many more; these were identified with analytics and had to be standardized before running any advanced analyses. In this instance we used a Teradata Aster platform for data loading, QC, and analysis. We also used generic Business Intelligence tools. The next step was visualization. Well bores are typically described in the depth domain. Figure 5 shows a set of data from a single well. However while this is useful for individual wells or clusters of wells in close proximity in the same field, it is of little use when trying to establish trends across a wider data set. The data is also multidimensional mixing metadata and continuous logs.

7 OTC MS 7 Figure 5: Sample depth-based plot of the data loaded for analysis (here for a single well). Note the Mud Weight and Deviation appear as near constant values for the depth interval shown An example of multi-variate analysis performed on the dataset is plotted in Figure 6. In this illustration there is drilling data (WOB plotted against ROP), geological data (the formations represented in a palette of colors), and borehole log data (the size of the data point shows the change from the nominal diameter). This plot successfully displays all the multi-dimensional data yet it rapidly becomes complex when looking at a large number of wells so we leveraged the iterative approach to focus on particular findings. Figure 6: Multi-dimensional analysis showing Drilling data points with colors representing the Formations and point size describing distance from the norm, the indicator of borehole quality Figure 7 shows a simpler type of display. The x and y axes are still the recorded drilling data, in this example WOB versus Torque, the size of the points depends on the value of DCAL but the colors show whether the point is considered as good (green) or bad (orange). The

8 8 OTC MS formation group and deviation are simply selected, so here is a group of wells from a specific region, both deviated and vertical. This is the display that was primarily used to analyse the data. Figure 7: An alternative Drilling vs. Wells plot where the color code indicates good quality boreholes in green (poor in orange) for a specified formation Results The results to this study were insightful both from a process viewpoint and for the answers to the selected business questions, How can I use historical data to improve efficiency and safety of future drilling operations? and Can we use Busines Analytics to answer Oil & Gas industry quesions? The study was delivered within the planned timeline, six weeks, with the largest component of that duration being the data collection and preparation. We were able to load all of the varied data and data types with no major problems and we then focused on the business analytics. We generated a geo-localized knowledge base with data from the UK Continental Shelf that exhibits some very specific correlations and information. These correlations are non-obvious for standard methodology as these data types would not be combined. The same answers could have potentially been discovered by detailed examination of all available reports so a tremendous amount of time was saved with this novel approach. The analytical discovery environment allowed for multiple quick iterations, hence multiple views were easy to create. An example was an anomaly seen on a specific plot that could be followed through other visualizations to point to an answer. In one plot a single well had an unusual response, well off-trend with the other data. It was also unusual in that it had a negative DCAL, i.e., the caliper was reading much less than the bit size. Looking at the single

9 OTC MS 9 well data plot it showed a very low caliper value, but there was no explanation until further research into the well reports and log images found the answer. The logging tool was sticking so the caliper arms, which were pushed against the wall with some force, were closed. This allowed the tool to continue moving up the borehole but for this interval the logged caliper was showing the tool diameter much below the bit size. It also resulted in a zone of suspect log data quality. We identified trends and patterns in the data sets across geologies/formations and across geographies. The geological variations were somewhat expected as some formation types are more prone to borehole problems than others. The geographical variation was not expected: there were well stability issues in some regions in specific formations while in others in the same formation there were none. Using the results generated we can produce an optimized set of drilling parameters for a given formation in a given area. Conclusions In this study we connected drilling data to well log data something that is not routinely done to answer the question Why do some boreholes increase or decrease from the nominal bit size? Our answer from a data set of circa 350 offshore wells in the UK showed clear correlations of borehole quality with drilling parameters, which were shown to depend on stratigraphy and region. Hence we showed that Big Data Analytics does indeed provide business value to the oil and gas industry. The data was preserved in granular details, Quality Controlled, and analyzed in an integrated analytical environment, which in our case can suggest optimal drilling parameters to avoid bad hole conditions in future operations. The next steps in this work will be to add more data both in extra wells but also in other controlling parameters such as mud weight and full deviation surveys. It will also be to add more analyses and make new connections between data types (e.g., core and pressure data). The implementation of Big Data Analytics will be essential to these endeavours.

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