A 3D stochastic model integrating depth, fault and property uncertainty for planning robust wells, Njord Field, offshore Norway

Abstract

The Njord Field, situated in the Haltenbanken area, is one of the most challenging reservoirs on the Norwegian shelf. The structural complexity is high, with numerous intersecting faults compartmentalizing the reservoir. The reservoir is dominated by alternating shale and sand intervals of tidal, estuarine and deltaic depositional origin. One of the main segments on Njord – the Central Area – was the target for a one-year improved oil recovery study. The Central Area reservoir is produced by depletion, with a current recovery factor of only 6%, as most faults appear to seal during production. The previous drilling experience revealed quite large uncertainty with respect to both depth and missing faults due to poor seismic data quality. In order to plan further wells in this area a model that comprises the full uncertainty with respect to both structural components (depth uncertainty and faults) and property components (facies, permeability, porosity) was made. The model was built by combining commercial modelling software (IRAP RMS and STORM:HORIZON) with an R&D tool (HAVANA) in a manner that has not been attempted previously, and 200 stochastic realizations were run. Each realization has a different structural 3D grid (depth and fault pattern) and different petrophysical properties. A streamline simulator in IRAP RMS was applied to rank the realizations and ten realizations were chosen for further well screening in the flow simulator (ECLIPSE). The model was applied to propose two new well targets that were robust economically (given all uncertainty). In addition, the model was applied in testing various unconventional well types, for example so-called ‘connector-wells’, which are open holes in the reservoir (but not connected to the surface), in order to connect fault blocks bounded by sealing faults. Drilling and subsequent seismic interpretation after the model had been built revealed that the structural uncertainty was even greater than predicted. It is crucial to capture the full interpretation uncertainty and, in particular, to address the problem of jump-correlation across fault blocks. Despite this, however, the observed cumulative production from the new oil producer compares well with the prognosis from the model.

Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

“…2. Structural interpretation uncertainty With poor seismic data, a single seismic image can produce considerably different structural interpretations depending on the different decisions made on horizon/fault identification [10]. Such an uncertainty from structural interpretation can be first order, especially when the structure is complex, as multiple interpretations can exhibit significant variations in fault intensity and fault pattern, which may strongly affect fluid flow behavior.…”

“…However, a method for inverting reservoir structure from production data under the constraints of prior geological information has been lacking or has relied on traditional parameter optimization. An attempt of dynamic data assimilation into stochastic structural uncertainty modeling is found in Rivenaes et al [10]. Their approach utilizes stochastic tools for perturbing horizons from a deterministic structural model and for simulating fault patterns.…”

“…The realizations that match historical pressure data are chosen by screening realizations using a streamline flow simulator. Their practice [10] showed that experts often give significantly different structural interpretations even based on a single seismic image; thus, the structural models should be built based on several structural scenarios.…”

Facies architecture of heterolithic tidal deposits: the Holocene Holland Tidal Basin

“…Although the mud laminae are not continuous to the bottom of the point bar (Choi et al, 2004), the tidal point bars thus show prominent permeability heterogeneity on the scale of the entire sediment body. The high amount of mud may severely hamper sand-tosand connectivity in case of a faulted tidal estuary reservoir, such as in the Tilje Formation in the Njord Field (Rivenaes et al, 2005). Here, even with small fault throws juxtaposition of sand and mud occurs and clay smearing may create sealing faults that compartmentalize the reservoir.…”

A parametric fault displacement model to introduce kinematic control into modeling faults from sparse data

“…However, the distributed damage associated with faulting perturbs the seismic imaging of faults (Sibson, 1977;Botter et al, 2014;Iacopini et al, 2016;Weinzierl et al, 2016;Botter et al, 2017) and the nearfault geometry is generally poorly constrained. This leads to uncertainties in oil and gas flow simulations and volume assessments (Hollund et al, 2002;Thore et al, 2002;Rivenaes et al, 2005;Richards et al, 2015).…”