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This paper reviews and shares lessons on the critical role of subsurface pressure data in the successful management of Agbami Field. Agbami is a Chevron operated deepwater asset located in 4,800 ft of water with a combination of water and gas injection development strategy. Production commenced in 2008 and has remained plateau at over 240 MBOPD. The field hosts intelligent well completions with capability of real time pressure data acquisition. In addition to this suite of pressure data from downhole gauges, wireline and LWD tools have been deployed to acquire valuable pressure data in the field.
Several applications of reservoir pressure data in Agbami Field are shared in this paper. First, pressure data was used in establishing connectivity between a water injector and an oil producer in a rather poor seismic amplitude area. This led to significant savings from drilling another injector to support the 20,000 BOPD producer. Second, a reservoir originally interpreted as "virgin", which was later found to be depleted is described. This reservoir re-characterization resulted in over 20% increase in oil recovery. In another case, oil-water contact in a major reservoir was estimated using wireline pressure data, resulting in the booking of additional proved volumes.
One key lesson is to always integrate pressure data with other data in the field. This integration often improves the asset team's understanding of the reservoir. A best practice is to ensure that all pressure data is quality checked before use to avoid erroneous interpretation. The availability of subsurface pressure data from different tools, at different gauge depths and at different times, poses a common challenge: RM practitioners who utilize these data need to ensure proper correction is done before field application.
Agbami is a prolific deep-water field which commenced production in 2008 and achieved peak production a year later, in 2009. Plateau production has been maintained in the field since 2009 through various efforts which include drilling of additional wells, pressure maintenance by gas and water injection, acid stimulation and choke/well-lineup optimizations. This paper focuses on the production optimization in the Agbami field obtained from choke and well-lineup optimizations.
Agbami production system is an intelligent network that comprises of completions, wells, subsea flowlines and risers. Currently there are 24 wells, 8 subsea flowlines and 8 production risers in the production system. Multiple wells flow commingled in each riser, but each well can be lined-up to either of two risers in a riser loop. Parameters such as pressure, temperature etc. are monitored in real time at different nodes in the network. Production optimizations are achieved by manipulating the production control chokes at the completions, wells or risers; and/or by changing the line-up of the wells.
Robust surveillance and monitoring are key to achieving successful production optimization. Periodic production tests are done at the completion, well and commingled riser level; while periodic pressure build-ups are done on the completion level. These surveillance data (plus real-time data) are used to update the completion, well and riser constraints and to calibrate the production network model. By continuously monitoring the operating constraints and comparing the constraints with the current production data, optimization opportunities are routinely identified. The identified opportunities are evaluated using the calibrated model and if viable, are proposed for execution. The key challenge is having good calibrated production network model to use for optimization.
Choke and well lineup optimizations have proven to be an effective approach to obtaining increased production at no cost. These efforts have led to an average production gain of 12,000 BOPD per year in the past 8 years in Agbami field.
A comprehensive reservoir simulation study was recently carried out for the A4 reservoir located in the Niger Delta. The A4 reservoir is divided into two fault blocks (Main and West) that are connected in the down-dip part of the reservoir both in the oil leg and aquifer. Original Oil-Water Contact (OOWC) was logged in the reservoir, but no Original Gas-Oil Contact (OGOC) was logged by the wells that penetrated the reservoir. Thus, there existed uncertainty in the OGOC from the Highest Known Oil (HKO) to the crest of the reservoir.
During the period of the simulation project, two oil producers (A4-1p and A4-2p) were producing from the Main Block, while one water injector (A4-1i) was providing pressure support. Two additional oil producers were then being planned to increase the recovery from the reservoir. One of the wells was planned to be drilled up-dip of the existing two producers in the Main Block, while the other well was planned to drain the West Block.
Base model deterministic history-match and sensitivity studies were conducted to gain insight into the reservoir performance and parameters that affect history match, especially the OGOC. Then, probabilistic history-matching was carried out to assess the full range of uncertainties of the different history-match parameters with special consideration to the OGOC.
Probabilistic history-matching shows that the P10 OGOC for the Main Block is about 24ft shallower than the HKO, which was also supported by the base deterministic model. The simulation models were then used to forecast the performance of the two additional planned development wells to validate the planned landing depth of the completions. The two additional development wells were drilled and brought online. Initial test results from the new development wells were consistent with the pre-drill base deterministic simulation predictions.
Agbami field is a deep-water field located offshore Nigeria. The oil production system is a complex intelligent production network that consists of 22 subsea production wells (19 wells are dual zone completion while 3 wells are single zone completion), 8 subsea manifolds, 8 infield subsea flowlines and 8 subsea flowlines/risers. A production network model for Agbami production network was built in GAP production modeling tool. The model consists of 41 inflow elements, 177 pipe elements, 68 chokes and 200 nodes with real-time pressure/temperature (P/T) measurements. Due to the large number of elements and P/T nodes in the model, it was very daunting to calibrate the model by unstructured manual tuning of the model calibration parameters. In fact, it takes several days to manually calibrate the whole production network. A structured computer assisted calibration workflow was developed to aid in fast calibration of the Agbami production model.
The structured approach to the Agbami production network model calibration used in this work is to first break down the model into 8 independent riser subsystems. Each riser subsystem is then further broken down into segments. The segments are zones, wells, infield flowlines, and flowlines/risers; with each segment having several elements and P/T nodes. Each segment of the model is independently calibrated using the latest production test data corresponding to that segment. A computer guided wizard was developed to sequentially match the P/T at different nodes of the segment using the Secant root-finding algorithm1. The calibrated segments are then coupled together into riser subsystems and each riser subsystem is then calibrated to the latest riser tests by manual adjustment of few parameters. The riser subsystems are further calibrated to the current conditions. The use of the structured computer assisted workflow has resulted in the faster model calibration time of within a day.
Ogbuagu, Frank (Chevron Nigeria Limited) | Afolayan, Femi (Chevron Nigeria Limited) | Esan, Femi (Chevron Nigeria Limited) | Obot, Nsitie (Chevron Nigeria Limited) | Adeyemi, Ganiyu (Chevron Nigeria Limited) | Okpani, Olu (Chevron Nigeria Limited)
This paper summarizes the strategy adopted in the development of two thin oil rim reservoirs in Okan Field, Offshore Niger Delta, Nigeria.
Its objective is to elucidate the strategy, engineering analyses, subsurface assessment and production procedures set in place to optimally develop the reservoirs.
Both reservoirs have oil thickness of <30 ft with gas thickness of >100 ft. The adopted development strategy for the two reservoirs involves the drilling of 4 wells, 2 in each reservoir, to drain the remaining oil reserves, prior to gas development.
Because of structural and fluid contact uncertainties, soft landing was incorporated into the well designs. Shale-to-shale correlation was used for accurate horizon depth prediction and detailed simulation models with local grid refinements were employed to determine optimum well orientation, landing depth, lateral length and aquifer properties. Details on their use to maximize value are shared.
While drilling, Azithrak™, a Baker Hughes tool, was used in geosteering the lateral well section to determine distance of well to nearest conductive zone as part of the oil-water contact tracking. All available data - logs, cuttings, reservoir pressures and production data - was incorporated and used to validate fluid contacts data because of the impact of landing depth relative to the fluid contacts on oil recovery. Simulation results and operational constraints were used to set acceptable production limits to ensure delivery of target reserves.
All the four wells have been successfully drilled and completed, with the wells landed successfully within the thin oil column, at the optimized distance from the fluid contacts, with the wells producing at <0.55 percent water cut. Initial production performances of the four wells are in line with static and dynamic assessment forecasts.
Lessons learned and challenges encountered during this development are also captured in this paper.
Even though the solutions of numerical reservoir simulation are pressures, production rates and fluid saturations; rarely are the fluid-saturations/fluid-contacts included in the history-matching process. History matching to all the available pressures, production rates and fluid saturations/fluid-contacts should increase reliability of a simulation model for forecasting. This history-matching concept was applied to a matured waterflood reservoir (H).
The H reservoir was discovered in 1970. It is vertically divided into two main zones (HA and HC).The reservoir started production in 1971 and has been under peripheral water injection since 1984. Nine producers and one injector have been completed in the reservoir. Currently only two producers (HC-104 and HA-111) and one injector (HAC-53) are active in the reservoir with a current recovery factor at 51%.
The H reservoir was history matched to the following observed data set: RFT pressure (4 wells), static well pressure (9 wells), flowing wellhead pressure (2 current producers), allocated well production (9 wells), and Fluid-Contacts/Fluid-Saturations (8 open- hole logs). History matching of the flowing wellhead pressure, which was done using flowtables, helped to resolve the gas-lift injection volume in well HA-111.
The simulation study was initially done without rigorous attempt to match historical fluid contacts from open-hole logs. Even though reasonable production/pressure match were obtained in some of the wells, the model produced excessive water in well HA-30 and could not achieve water breakthrough in well HC-104. The simulation was then improved by actively history matching the fluid-saturation/fluid-contacts from historical open-hole well logs. Good history match was obtained for pressures, production and fluid-saturation/fluid-contacts in the wells. This resulted in the identification of one new drill opportunity and three workover opportunities with a potential to increase the estimated recovery factor to 64%.