Flow units, connectivity, and reservoir characterization in a wave-dominated deltaic reservoir: Meren reservoir, Nigeria

Larue, D. K.; Legarre, H.

doi:10.1306/10100303043

Cited by 57 publications

(42 citation statements)

References 10 publications

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“…Generally, the Agbada section of the Yowi Field is made up of sediments deposited within the shoreface-shallow marine realm, composed of majorly stacked regressive barrier and shoreface complexes. Hydrocarbon is majorly confined to these deltaic-front sediments (Larue and Legarre, 2004;Jackson et al, 2005;Sech et al, 2009). This study documents the occurrence of thick stack regressive barrier and shoreface sands culminating in two structural highs on both the eastern and western flanks of the field as evident in reservoir N5.2 (Fig.…”

Section: Petroleum Potential and Exploration Implicationssupporting

confidence: 60%

“…Typical depositional architectures identified based on the stacking patterns include; beach, barrier, shoreface, mouthbar and channels. In most deltaic systems in the world, these depositional systems concentrate huge hydrocarbon deposits (Cook et al, 1999;Sydow et al, 2003;Husmo et al, 2003;Larue and Legarre, 2004;Jackson et al, 2005;Sech et al, 2009).…”

Section: Geobody and Depositional Environmentsmentioning

confidence: 99%

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Geobody Architecture and Petroleum Potential of the Yowi Field, Offshore Eastern Niger Delta, Nigeria

Momta¹,

Odigi²

2016

American Journal of Geoscience

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A study on geobody architecture and petroleum potential of the Yowi Field has been carried out by integrating well logs and 3D seismic data. The objectives of this study include: The determination of the lateral continuity of key horizons; identification of gross geobody distribution and architecture; and the petroleum potential of the field. Well log correlation shows that key horizons are continuous and laterally extensive across the field. Eleven sand horizons displaying majorly stacked upward-coarsening regressive log profile with high net-to-gross ratio towards the eastern flank of the field have been mapped within 7000ft thick (2333 m) interval. A depth structure map generated over the N5.2 sand reveals a structural saddle; an elongate four-way dip closure having two structural culminations. Further analysis using 3D seismic attributes revealed four facies distribution patterns. The Root-Mean-Square (RMS) and Variance attributes were applied to identify subtle lithological variations as indicated by its high and low amplitude anomaly. Four facies depositional patterns recognized from amplitude trends include: Dispersed facies pattern; linear/parallel facies profile; discrete/isolated pattern and meandering configuration. Architectural elements inferred from these amplitude distribution patterns include: Beachbarrier-shoreface depositional profile (about 7077ft (2355 m) in length, 1476ft (490 m) width on the eastern flank and 783ft (260 m) width on the west flank); deep sea channel morphology, chaotic/dispersed turbidite sands and stratigraphic pinchouts. Areas with highest Root-Mean-Square (RMS) amplitude values represent porous and sand-prone prospective units; whereas sections characterized by low amplitude reflections may be interpreted as deposits resulting from pelagic or hemi pelagic sedimentation, or a mud-rich and water-bearing facies. The structural closures form sites for hydrocarbon accumulation in the field. 3D seismic horizon slices remain the best amplitude displays for gross facies identification, selection of optimum drilling locations and for measuring facies architecture.

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Section: Petroleum Potential and Exploration Implicationssupporting

confidence: 60%

Section: Geobody and Depositional Environmentsmentioning

confidence: 99%

Geobody Architecture and Petroleum Potential of the Yowi Field, Offshore Eastern Niger Delta, Nigeria

Momta¹,

Odigi²

2016

American Journal of Geoscience

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“…3, with the geological hammer objects producing the best overall match to the production data. All scenarios have NTG values in excess of the percolation threshold (King 1990) and a simple porosity/permeability model such that the match quality becomes driven by dynamic connectivity issues such as tortuosity, as discussed in Larue and Legarre (2004). The production forecasts for the reservoir are produced before (Fig.…”

Section: Case Study 1: An Example Of Solving the Inverse Problem Withmentioning

confidence: 99%

Uncertainty Quantification in Reservoir Prediction: Part 2—Handling Uncertainty in the Geological Scenario

Demyanov

Arnold

Rojas³

et al. 2018

Math Geosci

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Models used for reservoir prediction are subject to various types of uncertainty, and interpretational uncertainty is one of the most difficult to quantify due to the subjective nature of creating different scenarios of the geology and due to the difficultly of propagating these scenarios into uncertainty quantification workflows. Non-uniqueness in geological interpretation often leads to different ways to define the model. Uncertainty in the model definition is related to the equations that are used to describe the modelled reality. Therefore, it is quite challenging to quantify uncertainty between different model definitions, because they may include completely different model parameters. This paper is a continuation of work to capture geological uncertainties in history matching and presents a workflow to handle uncertainty in the geological scenario (i.e. the conceptual geological model) to quantify its impact on the reservoir forecasting and uncertainty quantification. The workflow is based on inferring uncertainty from multiple calibrated models, which are solutions of an inverse problem, using adaptive stochastic sampling and Bayesian inference. The inverse problem is solved by sampling a combined space of geological model parameters and a space of reservoir model descriptions, which represents uncertainty across different modelling concepts based on multiple geological interpretations. The workflow includes building a metric space for reservoir model descriptions using multi-dimensional scaling and Geosci (2019) 51:241-264 classifying the metric space with support vector machines. The proposed workflow is applied to a synthetic reservoir model example to history match it to the known truth case reservoir response. The reservoir model was designed using a multi-point statistics algorithm with multiple training images as alternative geological interpretations. A comparison was made between predictions based on multiple reservoir descriptions and those of a single one, revealing improved performance in uncertainty quantification when using multiple training images.

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“…The offshore transition zone below the shoreface typically has good vertical permeability (k v ) but very poor horizontal permeability (k h ), although this can be improved by the presence of gutters and erosive scours in the sandstone beds (Brenchley et al 1986). Outcrops studies of shoreface sandbodies (Arnot et al 1997;Sech et al 2009) show that the discontinuous shales may be present within the shoreface often associated with low-angle clinoforms, but these typically have limited impact on fluid flow (Larue & Legarre 2004). Carbonate cemented layers and lenses may also be present, and these can have a more significant impact on production (Morris et al 2006;Leren et al 2010).…”

Section: Paralic Depositional Systemsmentioning

confidence: 99%

The application of outcrop analogues in geological modelling: a review, present status and future outlook

2014

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Analogues, especially outcrop analogues, have played a central role in improving understanding of subsurface reservoir architectures. Analogues provide important information on geobody size, geometry and potential connectivity. The historical application of outcrop analogues for understanding geobody distributions in reservoirs is reviewed, from the pioneering work of the 1960s to the high-tech virtual outcrop methodologies of today. Four key types of analogue data are identified: hard data, which describe the dimensions and geometry of the geobody; soft data, which describe the conceptual relationships between different geobody types; training images, which record the dimensions, proportions and spatial relationship; and analogue production data, which are taken from direct subsurface production analogues. The use of these different data types at different stages of the geomodelling workflow is discussed and the potential sources of error considered. Finally, a review of geobody and analogue studies in different clastic environments is discussed with reference to selected previous work and the range of papers in the current volume.Over the last 30 years, computer-based, geocellular models have become a routinely used tool for understanding subsurface reservoirs (Budding & Inglin 1981; review in Keogh et al. 2007). Such models are typically built to aid field development and reservoir management business decisions, but also serve the purpose of integrating disparate scales and types of subsurface data, and visualizing complex three-dimensional (3D) distributions of rocks and fluids. Since the application of early modelling tools, to the present day, it has been common practice to supplement sparse subsurface datasets (e.g. wells and seismic) with data and concepts derived from reservoir analogues. The intention of this process is to generate more accurate representations of the subsurface than would otherwise be possible. Considerable effort has been expended in recent years by industry and academia on the description of reservoir analogues for this purpose. This paper examines the challenges inherent in selection and application of appropriate analogue data, particularly quantitative datasets, during construction of geological models.The most commonly used type of analogue is the outcrop, where information on geometric data that are limited in the subsurface is more readily available in cliff sections. Analogues may also include subsurface data but this is less common. This aspect is considered briefly in this review. The perspective on the application of analogues as discussed here is with regard to sedimentology and stratigraphy applied to hydrocarbon reservoir analysis, and the insights that 3D facies models bring to the distribution of petrophysical properties that control hydrocarbon flow. The discussion is based on clastic systems, but many of the aspects described are equally as applicable to carbonates, although they are not discussed explicitly here. Other branches of subsurface modelling, no...

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Flow units, connectivity, and reservoir characterization in a wave-dominated deltaic reservoir: Meren reservoir, Nigeria

Cited by 57 publications

References 10 publications

Geobody Architecture and Petroleum Potential of the Yowi Field, Offshore Eastern Niger Delta, Nigeria

Geobody Architecture and Petroleum Potential of the Yowi Field, Offshore Eastern Niger Delta, Nigeria

Uncertainty Quantification in Reservoir Prediction: Part 2—Handling Uncertainty in the Geological Scenario

The application of outcrop analogues in geological modelling: a review, present status and future outlook

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