Summary

The Hamaca field, located in Venezuela's Orinoco heavy-oil belt, is a giant extra heavy-oil accumulation operated by Ameriven, an operating agent company for PDVSA, ConocoPhillips, and ChevronTexaco. Over the 35-year life of the field, more than 1,000 horizontal laterals are planned to deliver 190,000 BOPD to a heavy-oil upgrader facility. Reservoir models are built to support a broad continuum of activities to meet this objective. This paper will review the Hamaca reservoir-modeling process, the challenge of integrating many sources of geologic and geophysical constraints (including horizontal-well information), the focus on continuous model improvement, and issues unique to Hamaca rock and fluid properties. We will show that efficiently evolving a very large geocellular model in an active project like Hamaca can be accomplished through the use of object-oriented process automation. In addition, the paper will illustrate that careful consideration should be paid to issues related to horizontal-well sampling bias and positional uncertainty before constraining a geocellular model with horizontal-well data. The paper will discuss the multiple sophisticated modeling techniques that were used to address the objectives of the Hamaca modeling program.

Background

The Hamaca field is located in Venezuela's Orinoco heavy-oil belt, which is reported to contain more than 1.2 trillion barrels of heavy and extra heavy oil in a huge stratigraphic trap on the southern flank of the Oriente basin (Fig. 1). The Hamaca concession area, which covers 160,000 acres, contains 8 to 10° API gravity oil trapped in shallow fluvial-deltaic reservoirs of the Oficina formation (Miocene age). Sandstone reservoirs of the Oficina formation at Hamaca were generally deposited in a bed-loaddominated, fluvial-deltaic environment. Reservoir properties are excellent, with porosity values of up to 36% and permeability values of up to 30 darcies. Hamaca crude is considered "foamy" and is generally saturated with gas at reservoir conditions.1

Over the 35-year life of the field, more than 1,000 horizontal laterals are planned to deliver 190,000 BOPD to a heavy-oil upgrader facility, which is currently under construction.2 To date, more than 110 horizontal wells have been drilled to produce from the Hamaca reservoirs. Oil is being produced under "cold production" methods (no added heat) using progressing cavity pumps to bring oil to the surface. Cold production is possible because of the extended length of the horizontal wells (5,000 ft), excellent reservoir properties, and the foamy-oil nature of Hamaca crude.2 The heavy oil will be mixed with diluent just downstream of the wellheads to facilitate transport to the upgrader facility. The Hamaca crude will be converted to a sweeter crude product of approximately 26°API at the upgrader.

The combined use of both well and seismic data is critically important for characterizing the stratigraphic complexity of the Hamaca fluvial-deltaic systems. To assist in targeting sweet spots for horizontal-well placement, a 250-km2 3D seismic survey was acquired along with the drilling of 91 stratigraphic information wells with an average separation distance of approximately 1.5 km.

Modeling Approach

Hamaca reservoir-modeling activity has been motivated by strategic and tactical business drivers that include reservoir sweet-spot identification for development planning, reserves studies for a contractual acreage-relinquishment decision, and horizontal wellbore design and steering. The continuous flow of new information from stratigraphic and horizontal-well drilling programs and the analysis of original and reprocessed vintages of 3D seismic data have required flexibility in evolving geologic and engineering concepts.

"One Size Fits All."

An ambitious undertaking began in 2000 to build a single "evergreen" reservoir model that satisfied each of the Hamaca business drivers. The design goals in building such a model were to maintain a high level of consistency across all the operational activities in a resource- and time-efficient manner. Creating a one-size-fits-all model required that the model cover a large enough area (37×33 km) to satisfy strategic activities such as flow simulation for reserves assessments and sweet-spot identification, and that it be sufficiently detailed to plan and steer the horizontal wellbores.

In selecting the appropriate model-cell dimensions, the tradeoff between resolution and computational efficiency was considered. Some of the factors that weighed on the selection of cell dimensions were:

  • Sufficient sampling of the expected vertical and horizontal heterogeneity as defined by variography.

  • Accuracy in the simulation of pressure drawdown and associated production of gas in the vicinity of wells.

  • The ability to update the geologic model with new information with a 24-hour turnaround time.

  • Flow-simulation and history-matching turnaround time.

  • Hardware and software memory limitations.

After extensive testing, the optimal cell dimensions of 100×100 m in the horizontal and .6 m in the vertical were chosen, resulting in approximately 38 million cells distributed among 10 major stratigraphic units. For computational flexibility, major stratigraphic units were built as separate 3D grids (SGrids) and then merged as needed for flow simulation and well-planning and -drilling activities. Efficiently evolving such a large model as new information became available posed unique challenges that required a creative approach to the model-building process.

Process Automation.

The continuous inflow of new information from the stratigraphic-well drilling program, horizontal-well drilling program, and seismic interpretation required that Ameriven focus effort on building a reservoir-modeling process that is efficient, consistent, and repeatable. These goals were achieved by the use of automation.

Model-building automation was accomplished by the use of process scripting within an object-oriented modeling system. Scripts are files containing a sequence of commands that modify object properties and control object-to-object interaction. Nested layers of scripts were written to manage the entire model-building process including data loading, well-log processing, framework construction, region definition, reservoir parameter population, generation of numerous quality-control products, and export of the model to the simulator (Fig. 2).

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