E-mails: anamaria_2000@yahoo.com, david.davies@pet.hw.ac.uk, economides@houston.oilfield.slb.com This paper was prepared for presentation at the IADC World Drilling Conference held in Madrid, Spain, 5-6 June 2002. Copyright 2002, IADC Drilling ConferenceThis paper was selected for presentation by an IADC Program Committee following review of information contained in an abstract submitted by the author(s). Contents of the paper, as presented, have not been reviewed by the International Association of Drilling Contractors and are subject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the IADC, their officers, or members. Papers presented at the IADC meetings are subject to publication review by Editorial Committees of the IADC. Electronic reproduction, distribution, or storage of any part of this paper for commercial purposes without the written consent of the International Association of Drilling Contractors 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 where and by whom the paper was presented. WriteLibrarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-972-952-9435.

AbstractCurrent production technologies are demanding

not only a great effort to understand their technical engineering aspects but also novel approaches and evaluation methodologies to justify their investment and contribute in their implementation in a wide spectrum of geological, production and business scenarios.

Asset teams of the main operators and services companies working at international level need also management tools to search reservoirs with potential interest to apply new technologies worldwide. The production Geology approach has shown to be an effective tool that contributes in the implementation of complex production technologies in multiple production geology scenarios1.

Different sources of information were reviewed during the screening process to select reservoirs with appropriate geological, reservoir and production characteristics with potential to apply intelligent well technology. The review of the exploitation plans of key production areas worldwide allowed the identification of potential reservoir types with production problems or opportunities for which the implementation of intelligent technology is likely to be beneficial.

This new approach will help asset teams of the main operator companies to take technical decisions faster following their own development plans and business strategies. The applicability of this novel evaluation methodology to introduce production technologies at corporative level will be shown briefly trough a case study in Venezuela 1, 2.

Production Geology Approach

Current production technologies are demanding new procedures to measure, control and monitor large amount of engineering data. To optimize production in remote, deeper and hostile areas novel drainage strategies are requiring advance drilling and completion technologies 3,4,5. Current production seismic trends are oriented in the analysis of data at basin or perforation scale6. Non-conventional ways to analyze the geological information following these trends are therefore necessary.

Petroleum engineers are constrained in the search for production technologies applications to hardware and software development, technology evolution, engineering data availability/analysis and data confidentiality. However, due to the large amount of reservoir and geological information available in the open literature, production geologist may contribute in the design of exploitation plans looking in advance for reservoir candidates and geological constrains of each production technology. The production geology approach goal is to reduce knowledge gaps between production technologies and ge-ological information using knowledge management tools (figure 1). The challenges are:

To search for reservoir candidates to apply ad-vanced drilling & completion technologies

to identify the geological variables that impact economically any production technology in any reservoir scenario at any scale (basin, reservoir, well to well, perforation)

to contribute in the understanding of the geolog-ical aspects of production problems such as sand production, scale, migration of fines, formation damage, gas/water coning, hydrates

to participate in production & reservoir technology development projects

to cooperate in drilling, completion, develop-ment and production optimization projects using decision support tools for technology assess-ment

Production Geology Approach as a tool to accelerate the implementation of advanced drilling technologies: Intelligent well evaluation methodologyAna Maria Hernandez, SINTEF Petroleum Research, Norway; Dr. David Davies, Heriot Watt University, Edinburgh, UK; Dr. Christine Economides, University of Houston, USA.

The following information management tools were used in this paper to reach that goal:

Information databases Historic technological charts Knowledge maps (figure 2) Decision support tools for technology

assessment Screening Criteria Economic ranking matrix

Knowledge management allows a fast understanding of large amount of information related with a specific technical topic in short time. It is useful to build the tech-nical background required for multidisciplinary teams working on the implementation of production technolo-gies worldwide7.

This information will be useful as a frame where oper-ators can analyze drilling and completion technologies following their own exploitation plans and business strategies.

Intelligent Well Evaluation Methodology

The focus of this paper is to identify the geological variables that impact economically the intelligent well technology and their related production problems at basin, reservoir, well to well and perforation scale using decision support tools for technology assessment. The following evaluation methodology using the production geology approach is proposed:

1. Technological background2. Initial screening criteria: Production geology

scenarios with potential to apply Intelligent well technology

3. Geological constrains of Intelligent well systems at reservoir, well to well and perforation scale:

a. Down hole sensorsb. Isolated control flow zones

4. Intelligent well techno-economic options

Intelligent wells: Technological Background

To get a fast understanding of the intelligent well technology a series of information sources were consulted. Intelligent wells can be defined as complex instrumented wells with downhole devices that are connected remotely with reservoir management decision systems 8,9,10. Their main goal is to measure control and monitor real-time data.

The drivers for intelligent well systems appear to involve the following factors:

1. Rare or zero intervention

2. More completions per slot or per penetration

3. Underground gathering system sensors and controls

4. To reduce costs and/or risks.

The main components are isolated control flow zones, specialized chokes and valves, down hole sensors, intelligent artificial lift systems, specialized surface systems and telemetry technology. Some production scenarios where intelligent technology will increase economic benefits are 8,9,10,11,12,13:

Oil rims with gas/water coning problems in mature reservoirs

Complex Improved Oil Recovery projects that require monitoring of injection /production fluids such as water alternating gas (WAG)

Compositional heterogeneous reservoirs that require control of unwanted fluids (gas or water).

Improvement of reservoir drainage strategies trough production optimization

Heterogeneous reservoirs with pressure differential

New development plans in remote hostile offshore environment

To reduce well intervention cost mainly offshore

Intelligent wells to greatly accelerate ultimate recovery

Initial screening criteria: production geology scenarios with potential to apply Intelligent well technology

The search for possible reservoirs in key production development areas worldwide for intelligent technology has yielded screening criteria for candidate recognition. The analysis of the open literature allowed the identification of key areas with potential intelligent well applications.

According to the current offshore development plans worldwide and intelligent well technology applicability the following screening criteria was used:

Oil rims in heterogeneous reservoir with short-term, high economic potential interest for the main operators

A set of locations where IOR projects in complex reservoir are in progress with up to-date reservoir, production, drilling and geological data

Reservoirs with technology maturity, either with intelligent wells or where related technology (4D, Ocean bottom seismic, borehole seismic, down hole

sensors, complex wells among others) have been implemented

Remote offshore areas with economic potential where the reduction of intervention cost and surface facilities are necessary

Areas with technological potential under environmental regulations

Wide ranges of potential reservoir candidates to apply intelligent well technology at basin scale are summarized in the table 1 according to reservoir type, reservoir geology and potential applications 14,15,16,17,18,19,20,21,22,23.

In the British side of the North Sea, current efforts are associated to extend the life of mature reservoirs with complex IOR projects3 and the development of thin oil rims. In Norway, the main efforts will be done in secondary recovery projects in several reservoirs with complex fluid column, high lateral/vertical heterogeneity plus high internal heterogeneity4.

In the Gulf of Mexico, complex heterogeneous reservoirs with related technologies such as borehole and 4D seismic are evaluating the potential implementation of intelligent well technology with the goal to accelerate its implementation during the next five years24.

In West Africa5 many E & P development plans are in “under way” status, however, the construction of pipelines to connect Angola, Nigeria and Congo, the LNG plan in Angola, the construction of refineries in Angola & Congo and the gas to liquids projects in Nigeria are opening a wide range of possibilities to continue the exploitation of delta lobes, carbonates and turbidities oil and condensates reservoirs. In these key potential intelligent well development areas there are some geographical constrains:

1. Remote areas such as the Uk Atlantic Margin, West Africa and the Norwegian continental shelf will require specialized offshore technology to overcome related production problems such as water handling, sand production and hydrates processing3.

2. Increase in water deep are expected in the development plans for the next 10 years in ultra deep reservoirs (more than 5000 water deep) in the Gulf of Mexico24, USA; Barents sea, Norway and in Angola, West Africa. It will be necessary to improve the reliability of the technology.

3. Areas under environmental regulations such as the North Slope of Alaska, Liverpool bay 22 in the UK and the Coral reef Barrier of Australia

are planning to develop intelligent well technology to avoid well intervention and environmental economic sanctions.

Geological Constrains of Intelligent well technology at reservoir scale

To highlight the geological variables that impact economically the technology, the reservoir candidates were analyzed at reservoir scale (km-m). It allowed observing the most common reservoir types where intelligent well can apply and they are mature:

Structural reservoirs with high lateral and vertical heterogeneity

Tilted reservoir associated with salt domes Reservoirs with several degrees of

compartmentalization and connectivity due to changes in reservoir architecture

Stratigraphic reservoirs with bypassed oil zones

All of them present a critical zone for Intelligent well technology implementation: partially connected sands within irregular gas and water contacts in the middle part of the reservoir, usually in tilted structures. They are the result of changes in reservoir architecture between the lower and the upper part of it. It’s important to identify these zones in advance to optimize production and reduce production problems. However, they can be the best place for intelligent injectors/producers and sensors in heterogeneous oil rims and complex IOR projects if they are detected in advance. To improve the dynamic reservoir management at Km- m scale it is important to improve spatial target dimension and geometrical visualization. 4D seismic techniques combined with the new generation of sensors and Ocean Bottom Seismic (OBS) techniques will get a more

realistic visualization of geological data. However, 4D/4C seismic allow having 3D geometry and reservoir coverage but its resolution is limited. Borehole seismic systems have high resolution but geometrical limitation at km-m scale, which is needed to control and monitor, unwanted fluids in complex reservoir flow units within the reservoir types identified. Reservoir continuity is the geological variable that more impact at Km-m scale. Several potential applications to optimize observability and controllability following the reservoir continuity are visualized if permanent resistivity and electromagnetic sensors are implemented combined with 4C/4D seismic, borehole seismic, micro seismic ocean bottoms seismic, plus isolated inflow control valves 25,26,27,28,29. It is still necessary to reduce the data gap between seismic and core data and improve its resolution. Permanent down hole monitoring systems will play an important role in the new reservoir characterization “in

situ” and “virtual reservoir” trends. The need to speed up the reservoir knowledge reducing the uncertainties regarding to the everyday reservoir life and reservoir spatial distribution will have an impact in the dynamic reservoir management as a key part of the visionary instrumented field.A decision support tool for technology assessment based on Monte Carlo simulation was used to evaluate the geological variables that impact economically the intelligent well technology at well to well and perforation scale. It will be describe in detail in the techno-economic section.Geological constrains of Intelligent well technology at well to well scale

The following variables were evaluated in the reservoir candidates using Monte Carlo simulation at well-to-well scale (figure 3):

Vertical connectivity (m) Kv / Kh ratio Flow Units (m)

Reservoir flow units was the variable with more impact at this scale, therefore, it was reviewed in detail in the reservoir candidates because they are the geological architectural elements between wells that might be control and monitor using borehole seismic and also they are strongly related with the dimension, design and placement of the isolated control flow zones. In this paper a reservoir flow unit is defined in the following way:

Reservoir flow unit Rock types Rock wet ability Rock strength

It was noticed differences in the dimensions reservoir flow units in each geographical area (figure 4). Reservoir candidates in West Africa and Venezuela present high lateral and vertical heterogeneity but more homogeneous rock types (arenites with more than 95 % of quartz). Flow units associated with thick fluvial channels and submarines lobes can be found in the reservoir types described above. Their range is between +/- 200 - 20 meters in horizontal wells.By contrast reservoir candidates in Norway, Gulf of Mexico and Indonesia not only present high lateral vertical heterogeneity but also several degrees of internal heterogeneity (sublitarenites and litarenites with more than 95 % of rock fragments) and high clay content. In some cases can be considered associated to chaotic sedimentation mainly in

the Norwegian North Sea. The architectural elements that compose channels, bars and lobes in these geographical areas tend to be thinner with a range between +/- 50 – 5 meters in horizontal wells.The observations above highlight the necessity for sensors to improve the geometrical visualization and design of the isolated control zones in the range of 200- 0 meters. Permanent high resolution monitoring of changes in fluid saturation in the near well area and deep looking between wells using electromagnetic and seismic sensors in the well bore are recommended in all the scenarios. Reservoir under complex recovery process will be benefit with potential saturation movies that will reduce uncertainties in the well injector/producer location in compartmentalized reservoir. Heterogeneous reservoir with oil rims will increase the possibility to detect bypassed oil highlighting changes in the saturated volume permanently. The potential improvement in the knowledge of the drainage patterns in complex reservoir will optimize infill-drilling programs improving the sweep efficiency. Permanent monitoring will also open the feasibility for new production scenarios and new ways to do reservoir management in mature fields decreasing substantially drilling cost and extending their life. Permanent reservoir monitoring system will fill the need of many operators for reservoir uncertainty reduction in several ways such as reducing the target location. It will allow them work reduce the current “dimensional problem “ up scaling core data and downscaling seismic data, finally proposing a technology to work at the scale reservoir engineers need to improve their reservoir management understanding. All the reservoir types will be benefit with a permanent down hole system.

Geological constrains of Intelligent well technology at perforation scale

A decision support tool for technology assessment was used to evaluate the geological variables that impact economically the intelligent well technology at perforation scale. The following variables were evaluated in some of the reservoir candidates using Monte Carlo simulation:

Rock strength Rock type Rock wetability

These three variables compose a flow unit at perforation scale and they presented +/- the same impact. Rock type can be defined as the result of the combination of:

Textural attributes (grain size, shape, roundness and sorting)

Mineralogical variability

Clay composition

The analysis of geological variables that impact the isolated control zones is shown in the figure 5. Two important concepts arrive: completion windows and drainage points. The completion windows can be defined as the volume of rock that composes an optimal reservoir flow unit. Drainage points are the intervals that can be perforated following a reservoir flow unit. Bigger completion windows are found in West Africa and Venezuelan reservoirs and three potential well configurations are proposed: horizontal, high angle and multibranch wells. In them, it is possible to isolated reservoir flow units in intervals between +/- 200 –20 mts in horizontal wells. In the Furrial Field, Venezuela high angle wells perforated following the completion windows shown higher production (double in some cases) and less production problems than the vertical well perforated before in the same field 30. Isolated control zones of more than 200 meters are proposed in this type of well configuration. Smaller completion windows are found in the Norway, Gulf of Mexico and Indonesian reservoir due to their rock types (sublitarenites and litarenites). Heterogeneous reservoirs usually have between 10 – 15 rock types; however, it is possible to find reservoirs with even more than 50 rock types in the Norwegian North Sea related with chaotic sedimentation. These reservoirs have the smallest completion windows (+/- 10-5 m). Two possible well configurations will be high angle wells following the completion windows and long horizontal wells crossing small channels. They will be drilled in oils rims with a vertical section between 200 –40 meters so isolated control zones of high angle well might have +/- 100 meters in high angle wells, in horizontal wells they might be longer. In both cases, perforation optimization (very deep “rock type” perforations in the optimal completion windows) and individual control zones are suggested to get higher productivity and less production problems.

An additional problem that might have a high economic impact in the implementation of isolated control zones is the presence of intervals prone to scale or sand production. A previous study done in the North Monagas fields, Venezuela showed in the analysis of core information vs. perforated intervals that wells perforated in the completion windows had more production even if the perforated intervals were small, by contrast wells perforated in the “layers” (optimal completion windows plus sensitive intervals prone to sand or scale problems) had less productivity and more production problems 31,32.

Zones with extreme porosity /permeability values usually have the highest production of sand, and there is a geological explanation for that (the apparently good

sands, with biggest grain size that represent reactivation zones between cross bedding planes use to be the weakest sand intervals with lowest geomechanical strength). They might produce the early breakouts in the rock if they are perforated. Intermediate intervals will be very sensitive with any change in the flow regime if they are perforated. Zones with high clay mineral contents will have some plastic deformation and will tend to fail later and produce scale problems. The most resistant intervals seem to be cross bedding planes even if they are small, if the wells are perforated in these zones they will act as a filter.

A software the get the optimal completion windows during the perforation planning is suggested. Some possible analysis to upscale this perforation analysis to reservoir scale might be done analyzing reservoir pressure data vs. perforated intervals or micro-seismic data vs. flow zone indicators zones. Rock types are used together with wetability data to identify the flow zone indicators and the Amott wetting index to estimate the relative permeability curves in the reservoirs, therefore the geological perforation data might be extrapolated with engineering data.

Intelligent well technologies and advanced business models

To accelerate the identification of technical/economical value of intelligent well technologies the current criteria, advanced decision support tools and methodologies to justify new technology were reviewed. Traditional economical evaluations are:

1. - Discounted cash flow analysis33 that evaluate time value of money and investment opportunity

2. - Life cycle cost34 related with technology reliability evaluation

3. - Decision trees35 related with the identification of the economic threshold

4. _ Monte Carlo Simulation 36 that allow getting a forecasting and risk analysis and identification of sensitivities and economic drivers

The intelligent well economic projects goal is to get the economical impact that everyday events might have during the reservoir life and it require to analyze complexity, variability and uncertainty. To capture these events there is a necessity of more flexible techno/ economic decision support tools and adaptable methodologies to evaluate economically technology diversity in a more complex reservoir and business scenarios. Some of these new petroleum economic trends are:

Dynamic Complexity: Flexible management

models to capture complex conditions that create uncertainties over time in petroleum projects37.

Multi-prospects Evaluation: Probabilistic models that allow evaluating multiplayer prospects building and economical correlation matrix38.

Multi-objective Decision Analysis: It allows measuring technological benefits & financial performance ranking several technological alternatives in several scenarios39 (NPV + technology gain)

Techno-economic decision support tools using new Monte Carlo simulation capabilities of the Crystal Ball software

The last one was used in this paper. In the first place a decision support tools for intelligent well technology assessment was designed based on quantitative risk analysis using Monte Carlo Simulation40. The methodology is described in the figure 6. The first part was:

1. - to identify the geological variables that impact economically the intelligent well technology2. - to do the sensitivity analysis of each variable3. - to search the probability distribution to model each variable4. - to determine the decision variables and the assumptions that generate the variability and uncertainty5. - to define the forecast

The seconds step was to run the Monte Carlo Simulation using the following advanced tools of the Crystal Ball software:

Correlation matrix: Defines and automates correlations of assumptions

Tornado Chart: Individually analyses the impact of each model variable on a target outcome

Two-dimensional simulation: Independently addresses uncertainty and variability using two-dimensional simulation

It was useful as a techno-economic tool to evaluate the impact of the geological variables in the implementation of intelligent well technology41 (figure 7). It can be adaptable for the operators following their own development plans and business strategies. As the Intelligent well technology is the result of several production technologies a technical/ economical ranking matrix is proposed for the evaluation of the best technological practices in the same reservoir identifying the geological variables that have more technological impact in each reservoir scenario.

A flowchart showing an overall methodology proposed for an intelligent well project is shown in the figure 8. It takes into account the evaluation of technical, economical and reservoir scenarios adaptable to operator’s needs. Two bottlenecks for the acceleration of

intelligent well technologies implementation seems to be business and reservoir models, which don’t capture the current complexity, variability and uncertainty, and oversimplified models are far from what the industry already have to date.

Case study using the Production Geology Approach: Multilateral technology implementation in Venezuela

To accelerate the implementation of multilateral technology in PDVSA Venezuela, a multidisciplinary technology team investigated the technological constrains and their potential application during 1999 and it required a corporative domestic effort. The following corporative steps were used in INTEVEP-PDVSA, Venezuela to implement the complex well architecture technology at domestic scale with success:

1. Open literature review and generation of a bank of documents related with all the technical aspects of the technology accessible to the asset teams by internet

2. Discussion of all technical aspects, related with the technology with professionals working on the technology worldwide (operator of the main oil companies, services companies, research institutes, official institutions, universities) trough domestic and international forums.

3. Discussion of all case studies with the domestic asset teams in the reservoirs identified using the production geology approach via domestic forums.

4. Review of the domestic exploitation plans and candidate recognition to apply technology in the 2000-2006 corporative development plans. Discussion of the 20 potential cases identified with multidisciplinary asset teams via round tables and domestic and international forums.

5. Spread and divulgation of the technological information obtained to the asset teams at domestic scale by Internet using the information management tools previously described.

6. Pilot project to test technology reliability, performance and economic potential in the reservoirs with high technical-economical potential within the corporative project hierarchy

7. Technology massification

In this way, all the asset teams were able to get the technological background necessary to understand the technology and to analyze the potential application in their own exploitation units. All this effort was useful to implement the complex well architecture in Venezuela at domestic scale, changing 95% of failure in previous

multilateral wells to 95% of technological implementation success in just one year.

Conclusions

1. Intelligent well technology implementation can be accelerated only if multidisciplinary efforts using knowledge management tools to facilitate the understanding are undertaken.

2. A production geology approach was used in this paper with the goal to accelerate the understanding of the impact of the geological variables at reservoir, well to well and perforation scale on Intelligent well technology.

3. Screening criteria were established to identify reservoir candidates for intelligent well technology in key potential production geologies scenarios worldwide.

4. A critical zone for intelligent well technology implementation was identify at reservoir scale

5. To improve the geometrical spatial visualization and resolution of sensors are necessary to decrease the current data gap between seismic and core data, permanent seismic monitoring systems are suggest.

6. Geological variables that impact economically the implementation of Intelligent well technology were identified at reservoir (reservoir continuity), well to well (flow units) and perforation scale (rock type, rock strength and rock wetability).

7. The identification of completion windows and optimal drainage points to increase productivity and avoid production problems will highlight the best well placement and configurations increasing the performance of the isolated control zones.

8. The 0-200 meters scale is critical for measurement, control and monitoring.

9. A decision support tools for technology assessment based on advanced Monte Carlo simulation was useful to analyze the impact of the geological variables.

10. New business and reservoir models are necessary to capture everyday events related with intelligent well technology in complex reservoir and business scenarios.

11. Asset teams of the main operator companies working in offshore development plans may adapt this intelligent well evaluation methodology to evaluate their potential application following their own development plans and business strategies.

12. This paper presents an innovative approach for analyzing geological information to contribute in the implementation of the intelligent well technology and production technologies in general.

Acknowledgements

The author wishes to thank SINTEF Petroleum Research, Norway for supporting publication of this paper, mainly to the Intelligent Well Strategic Program leader Fridtjof Nyhavn and SINTEF director David Lysne. Many thanks to Dr. David Davies from Heriot Watt University, Edinburgh, UK, where most of the reservoir candidates information was compiled, to allow the publication of this paper. Special thanks to the Well Construction Knowledge community of PDVSA-INTEVEP, Venezuela for their support during the MTL project and to Dr. Christine Economides of the University of Houston, USA for her technical remarks.

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Table

Figures

Figure 1. Production Geology Approach

.

Reservoir Type Reservoir GeologyGeographical

AreaPotential IW Production Scenarios

OilDistal shore to shallow

marine sandstones

North Sea

UK

Pressure maintenance. Injector using

scaling (low PI/K)

OilShallow marine

Sandstones

North Sea

UK

HP/HT unconsolidated, ESP to increase

flow rates

OilPartially communicated

channalized sandstones

North Sea

Norway

Extended reach with smart completion to

control gas production

OilDistal deltaic sands

sequences

North Sea

Norway

Drainage optimization, zones with early

water influx, unsealing faults, sand

production

Oil Deltaic to shallow marineNorth Sea

NorwayImprovement of drainages strategies

OilBay fill thin bedded sand

sequences

North Sea

Norway

Improvement of drainage strategies

/extended reach wells

Oil Distal shore sandstonesNorth Sea

NorwayWater injection projects

OilComplex fluvial deltaic

sequence

North Monagas

Venezuela

Production optimization to reduce water

cut/ gas break thought WAG project

OilFluvial to near shore

sandstonesCanada

Multiplayer reservoir with pressure

differential

OilMarginal marine sands/

faultedCanada Poor quality, new recovery strategies

OilUnconsolidated deltaic

sandsOman

Heavy oil underlying by strong aquifers,

water/oil separators

Oil rim

With low GOR, aquifer

Triassic Sandstone /

halite’s

North Sea

UK

High K intervals, prevent water/gas

coning, high L/V heterogeneity, selective

isolation

Oil rims

Fluvial-deltaic to shallow

marine

North Sea

UK

Depressurization, complex contact

movement

Oil rim with gas capCoastal deltaic/ submarine

fans

North Sea

UKDrainage optimization

Thin Oil rim

With solution gasChalk sequences

North Sea

Denmark

High porosity chalk intervals, gas/water

coning

Oil/Gas to liquidsCoastal complex sand and

carbonates

West Africa

Angola

Low permeability formation, pressure

decline

Oil/ Gas to liquids Deltaic sand lobesWest Africa

Angola

High water cuts, scale inhibitors in water

injection

Oil/ Gas to liquidsTurbidities sand

sequences

West Africa

CongoWater deeper than 1400 mts

Oil/ Gas to liquids Deltaic sand LobesWest Africa

AngolaPotential gas condensate development

GasDistal shore to shallow

marine

North Sea

UK

Pressure maintenance. Injector using

scaling inhibitors to avoid formation

damage (low PI/K)

Dry GasTriassic Sandstone /

halite’s

North Sea

UK

High K intervals, prevent water/gas

coning, high L/V heterogeneity, selective

isolation

Dry GasIrregular Sands bodies in

salt tectonics

GOM

USAImprovement of well productivity

Wet gas, condensate Upper Cretaceous ChalkNorth Sea

Denmark

High porosity chalk intervals, gas/water

coning

Gas, condensate Deltaic to shallow marineNorth Sea

NorwayImprovement of drainages strategies

Gas condensateNorwegian continental

shelf

North Sea

NorwayPotential gas development

Rich gas, condensateComplex structure/ highly

stratified

North Sea

Norway

Overpressirized, fault transmissibility’s

affecting L/V pressure distribution

Compositional Shallow marineNorth Sea

NorwayImmiscible WAG injection

Cpositional

Distal deltaic tidal,

marginal marine, faulted

dome

North Sea

Norway

Improve producer injection locations,

heterogeneous K

EconomicsSoftware’s

Modelling Tools

Discounted Cash Flow

Databases

IW State of ArtField

experience

IW Related Technology

IWSt Reservoir Management

Telemetry

IWS technology

Technology Information

Development plans

IW Economics

Field Status

Technological Needs

Remote Sensors

Advanced wellTechnology

Companies working on IW

Worldwide

SponsorsPrevious

IW projects

Technical Books/ Magazines

Business Scenarios

Previous Technology

Reservoir Economics

Inflow control Valves

Reservoir Packages

AdvancedGeology

Production

Drilling &

CompletionTechnologies

Knowledge Management

KnowledgeGaps:

Time & Money

Target Forecast: Reservoir variability

Reservoir continuity (km-m) .44

Vertical connectivity (m) .50

Kv/kh ratio

.49

Flow units (m) .75

-1 -0.5 0 0.5 1Measured by Rank Correlation

Sensitivity Chart

Figure 2. Knowledge Maps

Figure 3. Sensitivity chart showing the geological variables that impact the intelligent well technology at well-to-well scale

Figure 4. Reservoir flow units dimensions in horizontal direction in reservoir candidates

+200 150 100 50 25 15 5 mReservoir Candidates

Norway

Gulf of Mexico

Indonesia

West Africa

Venezuela

Sand production proneScale prone

OptimalCompletion

Window

200-20 m

Reservoir flow units:Rock types + rock wetability + rock strength

Drainage points Horizontal well

Project EconomicsFlow Cash, NPV

ValvesChokesControl ZonesSensors

Identification of variables that produce economical impact

Variable 1

Variable 2

Variable n

Search of the probability distribution to model each variable

Determination of variables with high impact

Sensitivity analysis of each variable

Run the Simulation

Analysis of results

NPV

1141.17

45

5.6

11.57

100.00

40.5

11.23

6.16

1895.83

16

14.0

8.52

150.00

43.5

9.55

13.84

-200.00 0.00 200.00 400.00 600.00

STOIIP

Wells to drill

Plateau rate

Discount factor

Facility size

Recovery

Well cost

Well rate

Decision Variables

Assumptions Variability

Uncertainty

Forecast

InjectorInjection pointsValves/chokesZonal flow sensors

ProducerDrainage pointValves/chokes producerPermanent resistivity sensors

Interwell dataDistance between wellsCompleted intervalPerforationrock typesrock wettabilityrock strenghtpore pressurebarrierslayersSurprise handlingWater Breakout

Monte Carlo Simulation

Figure 5. Figure showing the completion windows, drainage points, sensitive production intervals and

reservoir flow units described in this paper

Figure 6. Decision support tools for technology assessment flowchart

Figure 7. Monte Carlo simulation to determine the variables with more economic impact at

perforation scale

Table 1. Summary of the different reservoir types and potential intelligent well applications

Sand production prone

Scale prone

OptimalCompletion

Window

200-20 m

Reservoir flow units:Rock typesRock wetabilityRock strength

OptimumDrainage points Intelligent well

Figure 5. Figure showing the completion windows, drainage points, sensitive production intervals and

reservoir flow units described in this paper

Figure 7. Monte Carlo simulation to determine the variables with more economic impact at

perforation scale

IW project Brainstorming

Identify Technical Issues related with

IW

Identify Economical

Issues related with IW

Identify IW Potential

Scenarios

Initial Options

Screening

IW production Optimization

Solutions

Prospective IW economic scenarios

Identify Critical Decision Issues

New business models

Discounted Cash Flow Analysis

IW Case history selection

IW Reservoir Modeling

Figure 8. Intelligent well project flowchart

Sensors &

controls

Decision Variables

Assumptions Variability

Uncertainty

Forecast

InjectorInjection pointsValves/chokesZonal flow sensors

ProducerDrainage pointValves/chokes producerPermanent resistivity sensors

Interwell dataDistance between wellsCompleted intervalPerforationrock typesrock wettabilityrock strenghtpore pressurebarrierslayersSurprise handlingWater Breakout

Monte Carlo Simulation