Improving Well Placement with Modeling While ... - Schlumberger

Improving Well Placement with Modeling While Drilling Increased computing power, the growing capabilities of modeling and simulation software, and human ingenuity across multiple disciplines are ushering in a new era in reservoir management. The ability to update reservoir models in real time will lead to exciting advances in wellbore placement, helping engineers and geoscientists improve field development.

Daniel Bourgeois Ian Tribe Aberdeen, Scotland Rod Christensen Oilexco North Sea Limited Calgary, Alberta, Canada Peter Durbin Ikon Science Limited Teddington, England Sujit Kumar Bogotá, Colombia Grant Skinner Stavanger, Norway Drew Wharton Houston, Texas, USA For help in preparation of this article, thanks to Adrian Kemp, Houston. Drilling Office, ECLIPSE, GeoFrame, InterACT, Osprey, PERFORM, PeriScope, PeriScope 15 and Petrel are marks of Schlumberger. Windows is a registered trademark of Microsoft Corporation. 1. Chou L, Li Q, Darquin A, Denichou JM, Griffiths R, Hart N, McInally A, Templeton G, Omeragic D, Tribe I, Watson K and Wiig M: “Steering Toward Enhanced Production,” Oilfield Review 17, no. 3 (Autumn 2005): 54–63. 2. Bryant I, Malinverno A, Prange M, Gonfalini M, Moffat J, Swager D, Theys P and Verga F: “Understanding Uncertainty,” Oilfield Review 14, no. 3 (Autumn 2002): 2–15. 3. Bratton T, Cahn DV, Que NV, Duc NV, Gillespie P, Hunt D, Li B, Marcinew R, Ray S, Montaron B, Nelson R, Schoderbek D and Sonneland L: “The Nature of Naturally Fractured Reservoirs,” Oilfield Review 18, no. 2 (Summer 2006): 4–23. 4. Ali AHA, Brown T, Delgado R, Lee D, Plumb D, Smirnov N, Marsden R, Prado-Velarde E, Ramsey L, Spooner D, Stone T and Stouffer T: “Watching Rocks Change— Mechanical Earth Modeling,” Oilfield Review 15, no. 2 (Summer 2003): 22–39.

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Sophisticated new LWD tools that help define the reservoir are being combined with fast reservoirmodeling software to optimize wellbore placement while drilling. This addition dramatically augments the traditional uses of reservoirmodeling and simulation tools, such as assessing reservoir performance, forecasting production and estimating reserves. Now, this combination helps improve hydrocarbon recovery by showing drillers where to drill more productive wells. Furthermore, data acquired while drilling can be added to the model to provide rapid updates. Through the years, the E&P industry has experienced the benefits of establishing a holistic view of the reservoir. This view is reflected in modern reservoir modeling and simulation software. One of the fundamental roles of these software tools is to simplify the complex issues regarding scale, data and uncertainty. Post-stack seismic data used in modeling define the interwell reservoir volume and characteristics and represent a static snapshot of the reservoir. Wellbore data from drilling and welllogging operations provide detailed near-wellbore information that can be interpolated away from the borehole and across the reservoir volume. Time-lapse, or 4D, seismic volumes are now used to monitor reservoir changes through time, examining reservoir dynamics. This often involves the mapping of seismic attributes derived from amplitude, phase and frequency content to highlight changes in the reservoir from one survey to the next. Models and simulators help in assessing and predicting reservoir performance and in identifying production problems. Although the terms “model” and “simulation” are often used

interchangeably, in the E&P business, there are important differences between them. Models, or conceptual models, attempt to represent actual systems and are largely static, but can be updated with new information. Simulators, or simulation models, attempt to describe how a system changes over time. Despite their differences, both reservoir models and fluid-flow simulators help engineers and geoscientists develop successful drilling plans, make completion choices, determine workover plans and formulate secondary-recovery strategies. The success of these applications relies on the accuracy of the reservoir models. In the last decade, drilling capabilities, along with MWD and LWD technological advances, have largely outpaced the industry’s ability to manage and rapidly exploit real-time data in modeling and simulation. Breakthroughs in drilling include accurate bit placement using a variety of new technologies, such as rotary steerable systems coupled with advanced LWD systems.1 Extended-reach, multilateral and geosteering technologies have increased the ability to contact more of the reservoir with complex wellbores. Tremendous volumes of highquality data are now acquired with modern MWD and LWD tools. Data can be transmitted to surface and immediately sent to centers of expertise for real-time interpretation. In many cases, borehole placement could be further optimized if the new information could be quickly integrated into reservoir models while drilling is still taking place.

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Crucial to developing this application are new software tools that enable a multidisciplinary approach to model building and updating, allow faster simulation using updated models and help asset teams evaluate risk as models and proposed well designs change with new information. This article investigates advances in reservoir modeling and simulation and their potential to improve wellbore placement. The contrasting roles of modeling in the past, present and future are briefly discussed, along with the visionary concept of simulation while drilling (SiWD). We demonstrate how rapid model updating has already helped operators place their wells more successfully in the North Sea. Next, we describe a recent test of SiWD capabilities and the improvements that are required for further advancement. Finally, we examine the potential applications of real-time modeling and simulation. Moving Modeling Forward One of the many challenges in developing a model is to strike a balance between the risk of high uncertainty and the cost and time needed to improve accuracy. When creating and maintaining an optimized model, reservoir engineers must consider data quality, quantity and

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uncertainty. The timing and frequency of incorporating new data into a model have an impact on the uses of models and simulations such as forecasting production, estimating reserves or planning the field’s development. For example, when incorporating critical near-wellbore LWD data, updates would need to be frequent to benefit the drilling of the subject well. While the model is being created and updated, uncertainty needs to be assessed.2 Reservoir modeling occurs at many levels; there are models within models. Geologic models focus mainly on geologic-layer thickness, depth and extent, but also include faults—a source of reservoir discontinuity and compartmentalization. Seismic and borehole data often provide the bulk of information from which to build and update a geologic model, including formation boundaries or layers. With data from well logs and cores, petrophysical models

describe formation lithologies and reservoir properties, such as porosity, permeability and fluid content. This same information gives geoscientists an appreciation of the variability within the reservoir. As reservoir complexity increases, for example, in naturally fractured or heterogeneous reservoirs, the relationship between porosity and permeability systems becomes more difficult to model.3 As the industry moves to drilling in more challenging environments, mechanical earth models (MEMs) become vital—most notably to avoid subsurface drilling hazards.4 In addition, PVT models are used to portray fluid properties across a range of phase-changing reservoir conditions. These models require input primarily from laboratory measurements, so rapidly updating this information in reservoir models may prove difficult.

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> Immersive visualization. Early visualization technologies were primarily used to interpret 3D seismic volumes. Today, the emphasis is on collaboration across multiple disciplines for visualization in well, reservoir and field management, including well placement.

Also, rigorous fluid-flow simulation helps describe complex multiphase fluid-flow phenomena in the reservoir. Production simulators also consider flow behavior outside the reservoir, such as phase slipping in the wellbore. Reservoir models and simulators have contributed to the oil and gas industry’s improved understanding and success in increasingly complex reservoirs. Nonetheless, building, maintaining and updating models are time-consuming

processes, which may involve numerous personnel across several disciplines. Recent changes in modeling methods and tools have made it possible to update models while drilling to influence drilling operations. Contrasting Approaches The established roles of modeling and simulation are to predict reservoir performance, forecast production and estimate reserves. Modeling and

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simulation are also commonly performed to determine completion and workover effectiveness and diagnose productivity problems by comparing actual production to predicted production, especially in horizontal wells. Moreover, fluid-flow simulation is crucial for developing infill drilling plans and formulating secondary-recovery strategies. While these important tasks do not necessarily require rapid decision making, accuracy is paramount to reduce uncertainty. One way to reduce uncertainty while drilling is to incorporate the most recent information as quickly as possible. To exploit new information while a well is being drilled, improvements were needed in several areas, including modeling and simulation software and hardware, and MWD- and LWD-data acquisition and delivery. In the past, computer processor speed had limited the ability to quickly and frequently update models and run simulations—especially full-field simulation that often takes weeks of computer and personnel time. Other factors have inhibited model building and updating. Throughout much of the 1980s and 1990s, the use of MWD and LWD data in modeling was inefficient, primarily because acquisition technologies and modeling and simulation software were not properly integrated. In addition, the process often was not automated and required human expertise. Most models were built in discipline silos. Some disciplines placed a higher priority on modeling because they used models more regularly and benefited more often from their use. Drillers used models less frequently, and as a result, their models were not optimized to solve issues related to drilling. This has changed; a general lack of cross-disciplinary integration has given way to the multidisciplinary asset-team approach, immersive visualization of the reservoir and real-time data delivery (above left).

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> Location of the Brenda field in the North Sea. Potential drilling targets in the Brenda field were identified using an advanced seismic preprocessing technique, a high-resolution velocity model, prestack seismic imaging and elastic-impedance analysis. Three wells have been completed in the Upper Balmoral sandstones, and production-test results have been encouraging. A fourth well drilled into the reservoir has been cased and is awaiting the drilling of the horizontal leg.

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Model Well Paths The Brenda field in the North Sea produces oil from a system of channelized turbidite sandstones (left). Individually, its reservoir sands are frequently too thin to be explicitly resolved by seismic methods, complicating exploitation efforts. Two 3D seismic datasets and information from 13 wells were available for field appraisal; these were used to generate a reservoir model. Modeling fluid flow in the reservoir using ECLIPSE reservoir simulation software suggested a four-well development program would be needed to optimize reserve recovery. To locate oil-

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bearing sands within the field, the operator, Oilexco, and Ikon Science used 3D seismic processing techniques with prestack data, including specialized elastic impedance computations and amplitude variation with offset (AVO) analysis. During 2006, Oilexco drilled three production wells and started a fourth well in the Brenda field. This four-well project targeted three sandstones in the Paleocene Upper Balmoral member of the Montrose group. In the first three wells, the true vertical depths ranged from 6,000 to 6,500 ft [1,829 to 1,981 m], whereas the total measured depths reached 13,700 ft [4,176 m]. Total reservoir thickness in these wells has ranged from 40 to 60 ft [12 to 18 m]. The top sand, the UB3, is usually of good quality but thin, and presents a difficult target to hit and stay within while drilling. The lower sand, the UB1, is also of good quality but is sometimes below the oil/water contact. The middle unit, the UB2, is thicker and more shale-prone, and is not a primary reservoir target everywhere in the field.

Seismic data were used to define the optimal landing point from which to start the horizontal portion of the wellbore. Given the challenging reservoir target, the relatively low resolution from seismic data, local dip variations of the reservoir top and the long horizontal wells used to exploit the reservoir, depth accuracy while drilling was a major concern. More specifically, depth errors between the bit and the model had to be resolved prior to landing the borehole near the proposed target. The targets were in areas defined by seismic imaging as exhibiting low elastic impedance, a good indicator of hydrocarbon accumulations in the Brenda field. To resolve depth errors, an Oilexco operations geologist would establish the actual drilling depths of two markers—the tops of the Sele and Lista formations—lying just above the Upper Balmoral sands, then compare the drilling depths to seismically determined depths and adjust the drilling path accordingly. Oilexco required that wells hit the reservoir while the wellbore was nearly horizontal—89° deviation—to ensure

optimal location of the well path within the reservoir. Immediately beneath the top of the reservoir, the 121⁄2-in. casing was set. A successful landing was paramount for the ultimate drilling objective—to maximize contact with the most promising reservoir. The horizontal leg was drilled using an 81⁄2-in. bit. As the wells were drilled, real-time boreholesurvey data transmitted uphole were delivered to Oilexco in Aberdeen, using the InterACT realtime monitoring and data delivery system, and then sent electronically to Ikon Science in London. These new data were incorporated into the Petrel seismic-to-simulation software model so that the current bit location could be displayed with respect to the desired targets in the model. Using these displays, the operations personnel in the Aberdeen Oilexco office could send proposed well-path changes back to the rig to optimize the landing of the well into the reservoir in preparation for drilling the horizontal portion of the well (below).

> Petrel well planning and visualization of Brenda field D3 well. The top map, used by the operations geologist for landing the well, shows the reservoir structure contours in black and white. Areas of low elastic impedance are shaded in light blue. Existing wells are blue and show the top of the reservoir as orange dots. The proposed D3 horizontal well path is shown as light green, and the actual D3 horizontal well path is in red. A Universal Transverse Mercator (UTM) grid on the map allows direct manual plotting of well coordinates. The bottom image shows the proposed D3 horizontal well path (light green) in 3D, together with existing wells (blue and red) that show formation tops. Pink spheres indicate the top of the Balder formation, light blue spheres show the top of the Sele formation, yellow spheres mark the top of the Lista formation, and orange spheres identify the top of the reservoir. The orange target boxes define the landing point and the XY limits of the horizontal path, and the white outlines surround areas of low elastic impedance that indicate a high probability of commercial hydrocarbons. These areas are draped onto the contoured 3D surface of the reservoir top. The arrow in the bottom right corner shows the north direction.

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> PeriScope curtain section for the Brenda field D1 well. This curtain-section display of Brenda Well D1 was used by the geosteering team to optimize the placement of the 1,800-ft [549-m] horizontal well leg (red, from left to right) in complex geology. It enabled them to stay predominantly within 10 ft [3 m] below the top of the Upper Balmoral reservoir. The lighter colors represent higher resistivity sandstone, and darker colors indicate lower resistivity shale. Without the PeriScope information, much of the proposed well path (blue-green) would have strayed into the shale, making the last half of the horizontal leg nonproductive. The image also highlights a low-resistivity zone from 10,750 to 11,050 ft MD. Missing the reservoir in this section allowed drillers to optimize pay-zone contact in the high-resistivity zones.

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> Creating a mapable surface from PeriScope results. It is possible to convert the PeriScope results that define the top of reservoir into a mapable surface using the distances from the borehole to the bed boundaries calculated from the PeriScope tool and borehole-survey data. In this example, the data are used to create surface sticks (pink) representing the boundary identified by PeriScope readings. From this a surface is created (red with black contour lines). The surface is not shown in areas where it dips below the wellbore.

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Once the wellbores were successfully landed in the top of the reservoir, Oilexco needed a more precise way to evaluate the reservoir sandstones and to locate the nonproductive shale immediately around the wellbore. To accomplish this, Oilexco used the Schlumberger PeriScope 15 directional, deep imaging while drilling tool (above). The PeriScope 15 tool is a deep-reading electromagnetic resistivity device that determines the direction and distance to bed boundaries by showing conductivity contrasts. With a transmitter-receiver spacing of 96 in. [244 cm], the tool has the theoretical capability to detect boundaries up to 15 ft [4.6 m] from the borehole. However, the actual resolved distance depends on the resistivity of the surrounding and adjacent beds and the complexity of the geologic layering. The PeriScope tool acquired data from around the borehole and successfully identified the reservoir ceiling and the presence of zones of lower quality within the reservoir, helping Oilexco to fine-tune the drilling of the horizontal wellbore. Adjustments were then made in steering the wellbore to maximize wellbore length within high-quality reservoir while maintaining the largest possible standoff distance above the oil/water contact at the base of the reservoir. Azimuthal polar plots generated from the PeriScope tool results show the bit position with respect to nearby bed boundaries, allowing the

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geosteering team to make real-time trajectory adjustments to optimize well placement.5 The bed boundaries defined by the PeriScope images could then be converted to Petrel surfaces and mapped within the reservoir model (previous page, bottom). The use of Petrel software to model the Brenda field facilitated rapid reservoir mapping—using geologic and geophysical data—and well-path design in one software package running on a standard laptop PC. Petrel software was instrumental in the Brenda field drilling workflow because it gave everyone involved the ability to observe the wellbore position with respect to the reservoir prior to and after landing, enabling efficient well-path design changes, avoiding plugbacks and sidetracks, and maximizing productivity. Oilexco has completed three Brenda production wells, which are currently being tied into the Brenda manifold. The completion flow tests for the first three wells exceeded Oilexco expectations. Their sandface productivity indexes and normalized flow calculations suggest a theoretical combined production rate of 44,000 bbl/d [6,995 m3/d] of oil.6 First oil is anticipated in late 2006 or early 2007. Operating companies around the world are increasingly using Petrel software to visualize the reservoir, make interpretations, evaluate risk and rapidly update the model while drilling, allowing them to optimize bit placement and produce more hydrocarbons. Modeling-whiledrilling workflows have also been successful in fields offshore Vietnam, India and Malaysia. Initially, visualization referred to seismic volume interpretation using high-power computers and was not connected to reservoir models. However, to fully understand reservoirs and fields, a wide variety of data must be analyzed and multiple disciplines must be involved. With Petrel software, a more comprehensive workflow is now possible using low-end Windows PCs at the desktop and in collaborative multidisciplinary environments. This allows asset teams to visualize, evaluate and assess complex relationships in 3D, and through time to better understand risk and uncertainty in multiple scenarios and more accurately predict production behavior. Modeling Ahead Specialized tools within Petrel software are tailored for modeling-while-drilling applications. For example, the Petrel Process Manager facilitates fast data loading and model updating by establishing an automated workflow. This

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> From seismic data to simulation. In this example, Petrel software has been used to visualize production data and perform history-matching, improving simulation and field development. The upper left image shows the reservoir simulation model with local-grid refinement around the boreholes. The upper right shows a horizontal well through the reservoir model with the seismic volume displayed in the background. The lower left shows the possible production-profile output from the ECLIPSE reservoir simulator. Each curve represents a different model realization created for an uncertainty study. On the lower right is a cumulative production plot at each borehole. The bubble size represents relative production volume. The Petrel workflow not only allows experts from multiple domains to meld their domain-specific information and knowledge into a single model-centric representation, but it also supports the ability to easily update and visualize the collective understanding as soon as new information is available. It is now possible to visualize, evaluate and assess complex relationships in 3D space and time to better understand risk and uncertainty in multiple scenarios and to more accurately predict reservoir flow behavior.

reduces decision-making and cycle time, and saves time and money. Well paths can be designed and updated using the Petrel Well Design tool, increasing drilling efficiency and bit-placement accuracy. The integrated workflow also can model log responses ahead of the bit along the proposed well path. Generating modeled petrophysical responses ahead of the bit helps asset teams understand the reservoir more fully and lets them choose the optimal well path in 3D, reducing uncertainty in complex settings. While some of these capabilities are here today in limited use, more widespread usage may be imminent. Many advances have enabled the move to modeling while drilling. For example, supervisory control and data acquisition (SCADA) systems, which have been in place for many years, allow immediate access to downhole data and control of downhole hardware. In addition, the new generation of reservoir simulators, which exploit faster, more sophisticated processors, has increased the computational power available to asset teams. Reservoir models

are now truly multidisciplinary tools that evolve as new reservoir or field information is acquired, such as new 3D seismic volumes, well logs, core data, well-test data or production-history information. Petrel software’s unique structure and functionality, coupled with its PC compatibility, facilitate integrated workflows in geology, geophysics, well engineering and reservoir engineering (above). Most reservoir models incorporate porosity and permeability only within reservoir sections and ignore the effects of overburden. MEMs contain stress, mechanical rock-property and porepressure predictions from the reservoir to the surface. Consequently, workflows frequently break down when the MEM is inadequate or nonexistent. Knowledge of overburden geomechanics greatly improves the well-construction process because, in part, it allows asset teams to assess the risks along a proposed well path and avoid hazards. 5. Chou et al, reference 1. 6. http://www.oilexco.com/news/060622.pdf (accessed September 29, 2006).

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> Saving time and money while reducing risk. Osprey Risk interactive software was designed to assist asset teams with well planning by providing probabilistic cost, time and risk assessment while incorporating geological and geomechanical models into the process. With subsurface targets identified, the well trajectory is designed in the Petrel Well Design tool (top) and input into Osprey software as a deviation survey. Earth-model data—at a minimum, pore pressures, fracture gradients and unconfined compressive strengths from the Petrel MEM—are also required input. The software proposes optimal hole size, bit type, maximum mud weights, and casing sizes, weights and set depths, taking into account production requirements, wellbore stability and many other factors. Using the available data and Monte Carlo simulation, drilling times and costs are calculated at key depths for a set of defined probabilities (bottom). This output can be used as a working operational plan. Significant drilling risks for the technical design are generated and can be displayed individually or grouped into categories of fluid gains, mud losses, stuck pipe and mechanical problems. A total risk index is also computed and can be used to rank scenarios. In the risk display, dark green indicates low risk, light green shows low to medium risk, yellow means medium risk, orange indicates medium to high risk and red shows high risk. Osprey Risk and its plug-in are fast-responding applications so that drilling engineers and geoscientists can easily change the design and compare results within minutes. This process leads to reduction of technical risk and highlights where mitigation strategies will be needed to implement the operational plan.

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In the past, exploiting the information in mechanical earth and reservoir models, including uncertainties, for practical drilling applications has not been straightforward. However, in 2000, Schlumberger Cambridge Research in England, as part of the MoBPTeCh industry consortium comprising Mobil Oil, BP Amoco, Texaco and Chevron, completed the development of a drilling-simulator prototype. This software lays the groundwork for more automated risk assessments in difficult drilling conditions.7 Today, the Schlumberger Osprey drilling risk prediction software and a Petrel plug-in enable critical risk assessment and drilling-cost and drilling-time estimates, in addition to providing a collaborative link between drillers, geophysicists and geologists.8 Following an efficient workflow, Osprey and Petrel tools allow asset teams to interactively design well trajectories and to update well-design plans as the model or proposed well path is changed (left). Another advantage of this software is that drilling engineers can customize the system to incorporate regulatory and company requirements, as well as local and historical experience. The industry is now considering the possibility of simulating reservoir response to new wells while drilling them. Along with the integration of real-time data into models and rapid model updating, the E&P industry is also benefiting from faster simulators. This is especially important when simulating complex fluid-flow and production behavior in large reservoirs, because these require large reservoir models. The need for dynamic evaluation while drilling intensifies as complexity increases. For example, simulating while drilling (SiWD) in three-phase, heterogeneous reservoirs already affected by nearby producing wells would be more beneficial than when drilling homogeneous, single- or two-phase reservoirs with zero dip—a case where using field experience might suffice. The idea of reservoir simulation while drilling, or dynamic evaluation, is not new. One such effort began in 1997 as part of a nearwellbore modeling project by BP, Schlumberger GeoQuest, Norsk Hydro and Saudi Aramco.9 This early project determined that real-time optimization of a well path is a true multidisciplinary exercise, requiring asset teams to have a clear understanding of the common goal and to be prepared for changing scenarios. Another finding was that real-time model updating should focus on the near-wellbore volume, where MWD and LWD data are most pertinent. Permeability distribution along the wellbore is critical to the predicted well

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performance. However, accurately capturing near-wellbore flow phenomena requires a smaller grid size and local-grid refinement, which decreases the processing time step while increasing processing time. Also, full-field simulation while drilling was deemed unrealistic in most cases because of the imposed time constraints during drilling operations. As part of this work, software was developed that generated a reduced near-wellbore model within the fullfield model and served as an early simulationwhile-drilling prototype tool. Well Performance to Define Well Placement In 2006, simulation while drilling (SiWD) was defined as a real-time optimization process to dynamically improve the design of the trajectory and the configuration and completion strategy of a well while it is being drilled.10 This concept has become more relevant today with the emergence of innovative drilling, MWD and LWD technologies that have enabled geosteering and advanced wells with elaborate trajectories, multiple branches or both. However, one of the key drawbacks in drilling these advanced wells was the level of uncertainty inherent in the initial reservoir description, including which fluids are present. This uncertainty accentuated the need for realtime data collection, integration and interpretation. SiWD has not been adopted for several reasons. The industry has only recently realized the advantages of real-time MWD and LWD data in well construction, reservoir and simulation engineering.11 In addition, the seamless integration of while-drilling measurements into modeling and simulation software tools has been difficult. Until recently, an integrated approach has been hampered by the lack of a proper platform from which multiple disciplines could work. Moreover, there has been a perception that model updating in the appropriate time frame was not possible. Finally, to be feasible, complicated workflows needed for real-time evaluation of multiple well trajectories and configurations will require automated optimization. Today, geosteering involves the interactive placement of the borehole based on geology and the desire to contact as much of the reservoir as possible, with the goal of optimizing the initial well productivity. While this technique has been successful, complicated scenarios require a more rigorous approach to effectively reduce risk. With today’s computing power and the increased ability to model critical factors while drilling, experts are envisioning the possibility of simulating well productivity ahead of the bit. Critical factors that impact short- and long-term

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> A closed-loop process for simulation while drilling. Defined by the frequency of measurements and the speed of optimization, the cyclical process includes data acquisition and interpretation, model updates, parameter changes and simulation until an optimal solution is determined; in the final step, appropriate action is taken.

productivity include well-completion options, multiphase-flow phenomena in the reservoir and in the well, the effects of drawdown at the wellbore, and pressure and fluid changes in the reservoir from neighboring production or injection wells. However, large, multilayer models make it difficult, if not impossible, to update, upscale and perform full-field simulations in time to impact simultaneous drilling operations. This problem is tempered by the fact that it is not essential to examine all regions of the reservoir equally when evaluating the future performance of a single well. For example, changes in reservoir pressure or hydrocarbon saturation at

great distances or in isolated layers might have a minimal effect on the subject well. There may also be minimal effects when the near-wellbore permeability distribution dominates the analysis. Here, semianalytical methods can provide fast and accurate results when modeling unconventional wells, but are less rigorous when dealing with multiphase-fluid flow and reservoir heterogeneity.12 A closed-loop process was tested to design, optimize and configure advanced wells in real time (above). To prove this concept, Schlumberger reservoir and software experts, along with Spectrum Consultores, started with a model based on data from a North Sea field. With

7. Booth J, Bradford IDR, Cook JM, Dowell JD, Ritchie G and Tuddenham I: “Meeting Future Drilling Planning and Decision Support Requirements: A New Drilling Simulator,” paper SPE/IADC 67816, presented at the SPE/IADC Drilling Conference, Amsterdam, February 27– March 1, 2001. 8. Givens K, Luppens C, Menon S, Ritchie G and Veeningen D: “Geomechanics-Based Automatic Well-Planning Software Provides Drilling Decision Support to Asset Teams,” paper SPE 90329, presented at the SPE Annual Technical Conference and Exhibition, Houston, September 26–29, 2004. 9. Bøe Ø, Flynn J and Reiso E: “On Near Wellbore Modeling and Real Time Reservoir Management,” paper SPE 66369, presented at the SPE Reservoir Simulation Symposium, Houston, February 11–14, 2001.

Bøe Ø, Cox J and Reiso E: “On Real Time Reservoir Management and Simulation While Drilling,” paper SPE 65149, presented at the SPE European Petroleum Conference, Paris, October 24–25, 2000. 10. Primera A, Perez-Damas C, Kumar S and Rodriguez JE: “Simulation While Drilling: Utopia or Reality?” paper SPE 99945, presented at the SPE Intelligent Energy Conference and Exhibition, Amsterdam, April 11–13, 2006. 11. Aldred W, Belaskie J, Isangulov R, Crockett B, Edmondson B, Florence F and Srinivasan S: “Changing the Way We Drill,” Oilfield Review 17, no. 1 (Spring 2005): 42–49. 12. Wolfsteiner C: “Modeling and Upscaling of Nonconventional Wells in Heterogeneous Reservoirs,” PhD Thesis, Stanford University, California, 2002.

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> Making simulation faster. Grid-coarsening, local-grid refinement and boundary-conditioning techniques are used to decrease simulation time while preserving sufficient resolution of reservoir heterogeneities and allowing multiple geostatistical realizations. In this North Sea example, the simulation model of a channelized reservoir (top) is optimized by upscaling each cell to a given resolution, which is defined by the local geologic heterogeneities, the degree of fluid-flow activity and the distance from the subject well (bottom).

the flux-boundary condition technique and localgrid refinement, the original 600,000-cell model was reduced to 30,000 cells, thereby simplifying the model (above). With a reduced-grid, near-wellbore model, several different well-path options were simulated over a six-year production period and compared based on three predicted outputs: water cut, oil-production rate and gas/oil ratio (GOR) (above right). Next, using the chosen optimal well path, the reduced-grid simulation was tested using a single-processor computer against the full-field simulation. Although the reduced-grid simulation yielded a slightly higher water-cut prediction over time, the predicted cumulative oil production and GOR were comparable (next page, top).

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The study proved that SiWD is feasible at typical North Sea rates of penetration—about 200 ft/h [61 m/h], depending on MWD and LWD operational requirements and BHA and bit configurations. In this study using a North Sea reservoir model, times for the various workflow steps were determined to be acceptable for a typical 10-day horizontal drilling operation in the field. However, time estimates vary because many of these steps are dependent on model complexity and size and hardware and software availability. Data acquisition and transmission were assumed to occur in real time. The steps included analysis and interpretation of the new information; updating the model; gridding optimization involving perpendicular-bisector

> Evaluating alternative well paths. For three proposed well trajectories, three inflowperformance predictions were used to determine the optimized well path: water cut (top), oilproduction rate (middle) and GOR (bottom). In this example, the simulation of Trajectory 1-2 (blue) terminates early because the higher water cut has exceeded the assumed surface waterhandling capabilities. Trajectory 1-1 (green) is optimal because it shows the largest cumulative produced-oil volume and results in the highest net present value.

grid processing and local-grid refinement; the initial new well proposal; and simulation runs using the near-wellbore model. In this example in a typical established field, the total estimated turnaround time was 20 to 30 minutes. Keeping It Real Time From an engineering perspective, simplifying models to enable modeling and simulation while drilling is not necessarily the answer. However, simplifying the workflow is always a positive step. Software tools continue to become faster and easier to use, connectivity to remote locations is increasingly reliable and larger data volumes are being transmitted at higher rates from downhole tools to end-users as technologies improve.

Oilfield Review

Cumulative oil, bbl/d

7

Water cut, %

6 5 4 3 2 1 0 01/04 12/04

12/05

12/06

12/07

12/08 12/09

2.0E+07 1.8E+07 1.6E+07 1.4E+07 1.2E+07 1.0E+07 8.0E+06 6.0E+06 4.0E+06 2.0E+06 0 01/04 12/04

1,400 1,200 1,000

GOR, ft3/bbl

8

600

Full-field simulation Near-wellbore simulation

400 200

12/05

12/06

12/07

12/08 12/09

Date

Date

800

0 01/04 12/04

12/05

12/06

12/07

12/08 12/09

Date

> Comparison of SiWD results to full-field simulation results of Trajectory 1-1. The optimized, near-wellbore simulation produced results similar to that of a full-field simulation of Trajectory 1-1 production rates over six years. Initially, the reservoir volume described in the near-wellbore reservoir model has enough energy to match the full-field simulation volume results. However, after the initial three years in the near-wellbore simulation, the absence of pressure support from the full reservoir volume shows up as relative permeability beginning to dictate fluid movement.

Wellsite data acquisition, aggregation and display

Depth data GeoFrame Petrel InterACT hub Operational data

Schlumberger OSC InterACT data hub

Rig sensors

Specialist services No Drilling Surprises PERFORM Geosteering Remote monitoring Remote control Client asset team

Time data

Trajectory data Drilling Office

Downhole tools

> Real-time workflow. Secure, real-time transmission of downhole data from remote drilling sites is accomplished using the InterACT system or third-party servers (left). Experts at the Schlumberger Operation Support Centers (OSCs) use this timely information and specialized software tools to help operators monitor and analyze crucial drilling, geological and geophysical data; avoid drilling hazards; hit reservoir targets; and remotely control drilling operations (center). Throughout the process, a wide range of data, including depth, time, operational and trajectory data (right), are used to update models, run simulations and identify the appropriate actions.

Another indication that the implementation of modeling and SiWD will increase is seen in the growing number of resources dedicated to realtime drilling solutions. Schlumberger Operation Support Centers (OSCs), for example, are now distributed worldwide to remotely monitor, model and control drilling processes. These centers are staffed with experienced personnel armed with powerful software to help operating companies minimize drilling risks and achieve their drilling objectives in a collaborative setting (above).

Winter 2006/2007

While the results have been encouraging so far, several areas need further work. Well-path trajectory optimization would be improved with the development of automated well-path selection algorithms. For complete optimization, more consideration of downhole completion systems is needed to better simulate the inflow performance. The increased use of artificial intelligence techniques should continue to be explored. There remains a need to couple fluid flow and geomechanics in SiWD. In addition, integrating surface and subsurface simulations would improve the accuracy of production predictions, although this would add a significant

amount of time to the process. Finally, more work is needed before SiWD in fractured and other complex reservoirs becomes feasible. Modeling dual-porosity and -permeability systems and the complex interaction between fractures and the matrix is challenging, even without restrictive time constraints. The advances in modeling and simulation software and hardware, coupled with the E&P industry’s increased understanding of complex reservoirs and complex wells, will create a more fertile environment for optimizing well placement while drilling. —MG

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