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By Kehinde Ladipo (Ph.D.)

Geoscientists have identified  10 Cretaceous sedimentary basins in Nigeria.

One of them, the Dahomey Basin, is located along the coastal frame in the country’s southwest.

The remaining nine (9) are  onshore systems.

All of these  basins have either proved to be active petroleum systems, or have the potential to prove the presence of hydrocarbons especially the Cretaceous systems along the Benue Trough that is rapidly emerging as a significant and promising petroleum province in recent times.

The first well in the Benue Trough region, Kolmani River-1 in the Gongola Basin, was drilled by Shell Nigeria Exploration and Production Company (SNEPCO) to a depth of about 3000m in 1999 and it encountered ca.33 billion cubic feet (bcf) of gas and little oil. Two other wells, Kuzari-1 and Nasara-1, drilled by Elf Petroleum Nigeria Limited (TotalFinalElf) in 1999 to a depth of 1666m (1.67km) and Chevron Nigeria Limited (Chevron Texaco) in 2000 to a depth of about 1500m (1.5 km), respectively, were reported dry.

With the discovery of Ogo field in the Dahomey Basin in 2013 and the appraisal of Kolmani River in 2019 with an estimated reserves of over 700 – >1BBoe the promise of a prolific series of contiguous petroleum systems may have become real along the ca. 1500km stretch of the Benue trough that extends from the Dahomey basin in the south and as far as the Bornu/Gongola Basin in the northwest.

Significant is that these more recent finds came into light more than 20+years after these basins have been proven to be working petroleum systems. The gamechangers that led to the discoveries are no different from what geologists have always known to be drivers of exploration: data, e.g. new concepts of the hydrocarbon habitat, new high fidelity data and technology which  improve the understanding of the petroleum systems of the subsurface, reduce  evaluation cycle time and, in the event, lower the exploration finding cost.

Over the last few decades, the renewed interest in exploration for oil and gas potentials in rift basins across the African continent has significantly changed the landscape of hydrocarbon provinces and their prolific nature. Origins of these rift basins were associated with the break-up and drifting of the continental masses which started in the Late Jurassic up till the early Cretaceous. The inland sedimentary basins of Nigeria comprise the Benue Trough as well as adjacent conjugate systems such as the Dahomey, Bida and Anambra Basins and the Lower Benue, bounded by the Romanche Fracture Zone to the west and the Chain and Charcot Fractures in the east.

“The data already acquired by NNPCL needs to be made available to prospective companies while technology vendors must be encouraged to ensure adequate knowledge transfer if the nations target is to be achieved in order to unlock the potential of the Cretaceous Systems in Nigeria’s inland Basins.”

Timing of movement of major fracture zones and associated secondary fault zones remain one of the key uncertainties, however the propagation of the fracture zones which align with basement fractures appear to vary. While the coincidence in alignment of the Chain and Charcot Fracture Zones with the margins of the Benue Trough suggest propagation into the basement, other secondary transform fractures i.e. the Benue Fracture zones were limited and may have resulted in the restricted nature of the adjacent Dahomey, Anambra and the Lower Benue systems. Conjugate northwest-southeast shear faults during extensional phase of the late Jurassic bound these southern basins to the north and also subdivide the Benue Trough system into mini-basins and tectonic provinces. Drifting of the African plate was limited and the westerly rotation of the continental rigid mass, possibly around the ‘fulcrum’, located around Kribi Fault Zone. This terminated the opening of the Benue Trough and initiated the first transpensional phase as well as the reactivation of shear faults within the sedimentary basins.

Mini basin architecture of the Lower Cretaceous rift systems, Dahomey Basin southwestern Nigeria and tectonic elements of the Lower Cretaceous of southern Niger (courtesy Lekoil and Shell)

The geometries of the Cretaceous basins differ along the entire length of these conjugate basins; from distributed half grabens in the southern basins to hyper-extended rifts in the northwest.

However they form excellent depocentres for deposition of lacustrine source beds, reservoir sands and seals within mini rifts during the entire fill histories of these basins. This new understanding of hydrocarbon systems, where the play elements i.e. source rocks, reservoir rocks and seal occur in a contiguous manner within a well-defined mini basin depocentre, is key to the prolific nature and prospectivity. Continuous sediment inputs from prograding  paleo delta across the basement highs resulted in progressive infill of the mini-basins by fill-and-spill mechanism. Timing of infill of min-basins may vary, but the stratigraphic architecture are similar: comprising lacustrine shales, alluvial fan and delta.

The interpretation of three dimensional (3D) seismic data in the Dahomey Basin suggests that individual mini basins can exceed 5-6kilometres in depth, suggesting that basal lacustrine source rocks may have reached the generative window. There are also contributions of even deeper source rocks, including the late Cretaceous and Tertiary shales where these become buried below the oil generative window. These mini basins can be more than 5-6kilometres deep, making each sub basin capable of generating hydrocarbons. In the Dahomey Basin, successive mini basins can be more than 20,000feet deep due to the regional tilt to the east-southeast against the main Benue Fracture zones (i.e., the Okitipupa Ridge). This has significant implications for continuous hydrocarbon charge, timing and expulsion from the basal lacustrine source rocks, the Ise Formation, as well as from late Cretaceous and Tertiary source beds that reach maturation depth and temperatures. Although the maximum depth of burial along the Benue Trough remains unknown, this could be significantly deeper than currently estimated i.e. ca 6-7 kilometres, especially with the regional tilt to the east towards the axis of the Charcot Fracture Zone. This also suggests that charge will be continuous with significant contributions from younger late Cretaceous and Tertiary source rocks which have become buried deep enough to reach the oil window.

Results of the recent hydrocarbon systems analyses of selected wells in the offshore Dahomey basin indicate that hydrocarbon generation commenced around 100Million years  and peaked at ca.90Million years (assuming the Neocomian age source rock i.e. the Ise Formation. However, it must be pointed out that depth variations of the individual synrift blocks and differences in fill histories will suggest that the maturity window will be reached at different times and consequently multiple phases hydrocarbon charge resulting in complex oil and gas fill patterns.

In the Dahomey basin, offshore southwest Nigeria the availability of 3D seismic data over the Aje and Ogo fields has been largely responsible for the discoveries and the new thinking of the play concept as well as a robust portfolio of identified and defined prospects as well as the several identified-undefined leads which, together, define a west-east trending ‘sweet spot’ that can guide future prospectivity. The extension of the play concepts suggests that up to 20-30Billion barrels of oil equivalent may remain as the undiscovered potential. The Dahomey Basin therefore provides an excellent analogue to guide exploration in the inland basins of the Benue Trough to unleash the huge hydrocarbon potential along the ca 1500kilometre axis.

Prospecting licenses for blocks within these inland basins have been awarded by the regulatory agencies in the effort to drive and achieve the nation’s target of 40Bllion reserves and 4Million barrels production. However, several challenges hinder the much-desired exploration effort by license holders, primarily the lack of, as well as the high costs of acquisition of 3D seismic data to fully define and generate consistent subsurface models of the hydrocarbon systems and reduce exploration risk. Very recently, the acquisition and interpretation of high resolution enhanced full tensor gravity (eFTG) as well as magneto-telluric (MT) data has led to the successful appraisal of Kolmani River play that proved prospective resource volumes currently estimated to be in excess of 1Billion barrels of oil equivalent (BOE). The fast acquisition and interpretation techniques, with capabilities to incorporate existing subsurface data sets, available seismic, well and stratigraphy in the workflow further improves the understanding of the subsurface model in play analyses of the hydrocarbon habitat as well as the risk assessment, both at the basin play level and prospect evaluation. Currently these technologies are proprietary and available with specific vendors however they have proven to have major advantages in reduction of exploration cycle time, significantly lower cost savings and implications for portfolio high-grading and ranking of opportunities within defined hydrocarbon sweet spots.

“Results of the recent hydrocarbon systems analyses of selected wells in the offshore Dahomey basin indicate that hydrocarbon generation commenced around 100Million years  and peaked at ca.90Million years (assuming the Neocomian age source rock i.e. the Ise Formation. However, it must be pointed out that depth variations of the individual synrift blocks and differences in fill histories will suggest that the maturity window will be reached at different times and consequently multiple phases hydrocarbon charge resulting in complex oil and gas fill patterns.”

Rift basins across the African have significantly changed the petroleum landscape of the continent, especially over the last three decades, contributing over 30% of the world’s hydrocarbon resources. The Benue Trough has potential to be highly prolific. Current efforts by the NNPCL to open up more tectonic provinces along the Benue Trough such as the Middle Benue, the interpretation of recently acquired non-seismic high frequency gravity and magnetic data using new 4^th generation interpretation techniques and workflows will be key. The data already acquired by NNPCL needs to be made available to prospective companies while technology vendors must be encouraged to ensure adequate knowledge transfer if the nations target is to be achieved in order to unlock the potential of the Cretaceous Systems in Nigeria’s inland Basins.

The author, Kehinde Ladipo FNAPE, FNMGS, FAS,  a  Consulting Geologist with over 45 years of professional and  scholarly practice, will receive the  Aret Adams Award, the highest intellectual  and life time achievement honour of the Nigerian Association of Petroleum Explorationists (NAPE) on November 11, 2024.

By Dr. Ian F. Jones¹, Dr. John Brittan¹, Johnny Chigbo¹, Dr. Gloria Awobasivwe², Christopher Osolo² and Paula Ukerun² 1: ION Geophysical;  2: Bulwark Services Nigeria

Introduction

In this review article, we consider the development of subsurface parameter estimation for use in building subsurface images for geological interpretation and reservoir evaluation using seismic migration procedures. We outline the progressive evolution form a purely linear approach wherein no reference was made for consistency between the various steps in the procedures, to the emerging approach of a closed-loop iterative workflow wherein each step is checked for internal consistency, and the parameter fields updated until all steps in the procedures are mutually consistent (Brittan and Jones, 2019).

In seismic data processing we aim to: separate ‘signal’ from ‘noise’; build an anisotropic velocity model; migrate the data, producing ‘true amplitude’ angle classes that are then used for elastic parameter estimation for reservoir characterisation. The parameter estimation techniques we have are limited in their resolution, and this restricts the accuracy and precision of the images we can produce. In general, with conventional tomographic velocity update we are limited by the ray-theory ‘scattering limit’ to a resolution of perhaps 5x the available sound-wavelength. Hence we are modest in what parameters we try to estimate tomographically, at best obtaining a smooth anisotropic velocity field suitable for migration, with features with lateral scales of about 500m.  Many excellent results have been obtained with ray methods, and developments (such as well and structural constraints) continue to improve them. Conversely, parameter estimation using ‘full waveform inversion’ (FWI) can perhaps deliver resolution of about half the available sound-wavelength, so theoretically perhaps ten-times the resolution of ray methods. FWI primarily uses the transmitted (refracted) wavefield rather than the reflected wavefield, and typically ignores density contrast, attenuation (Q), etc., (e.g. Virieux and Operto, 2009; Jones 2010, 2018).

For the majority of geological environments, building a model with FWI will not result in an image that is radically different than that obtained using tomography. The exception to this observation would be in shallow water with small-scale anomalies (e.g. over-pressured gas), or for deep subsalt reservoirs (and then only if we have low frequencies and long offsets). Along with better well-ties and potentially better images, the additional promise of FWI is in delivering high resolution interpretable attribute fields directly and quickly. 

Here, we’ll assess the development of the data processing procedures involved in obtaining subsurface parameter fields for use in migration, and consider how these methods have developed over the past thirty-odd years, and what their future development direction might be.

Figure 1. The ‘S-curve’ development profile for any technology development, in the context of tomography and FWI.

Historical Background

In the life-cycle of any technological development, there is a slow development phase when our initial understanding is poor but growing, followed by rapid development once the fundamentals of the process are fully comprehended, and finally further development levels-out as the method is then fully exploited (Figure 1).

Historically, seismic data processing workflows were purely linear. Field data were ‘processed’, a rudimentary velocity model was estimated from stacking velocity picking, using map migration to depth-locate picked time-horizons, and migration was performed. These tasks happened just once.

From about 1995 onwards, with the introduction of ray-based tomography, the model building element changed to become doubly iterative, in that repeated ray-trace modelling was utilized within an inversion scheme, so as to converge on a model that produced flat common-reflection-point (CRP) gathers, after several iterations of migration. As it is comparing data after migration, this technique is an ‘image domain’ inversion scheme, and hence it is not looking for consistency with the raw input data.

Since about 2005, FWI has been gradually introduced, modifying the tomographic solution by using wavefield-extrapolation modelling so as to iteratively match forward modelled synthetic data with measured field data. Hence, this methodology does indeed ‘refer back’ to the raw input data, but as the inversion is performed in the ‘data domain’, and still has limiting assumptions, the resulting model is not guaranteed to produce ‘flat gathers’ in the ensuant migration

What are the respective parameter resolutions available from these two methods? The resolution of ray-tomography is limited to cell-sizes of perhaps 100*100*25m, as on scale lengths smaller than this, the ray theory approximation fails. Conversely, waveform methods use a cell size of potentially less than 15*15*15m … hence we can expect much better lateral resolution in the parameters in the near surface, and overall better resolution in the deep section.

However, none of these approaches and subsequent migrations attempted to compensate for the underlying ‘bad physics’ or ‘bad data’ that we were employing. For example, using a one-way acoustic wave equation, and with field data that are poorly and/or irregularly sampled, and containing remnant multiples and possibly mode-converted energy. Hence, the least-squares migration technique was introduced to attempt to compensate for some of these issues, in that another iterative inversion loop is introduced so as to form an image (and/or gathers) consistent with the input field data (Schuster, 1997). However, this does not simultaneously try to modify the subsurface model, and still assumes that data are multiple-free.

Figure 2. Flow chart representation of: a) the historical ‘linear’ approach to imaging, b) image-domain ray-based tomography, c) data-domain FWI, d) least-squares update (which can be applied in either the image or the data domain). (Adapted from Vershuur and Berkhout, 2015).

Figure 2 outlines the above methods in a series of flow-charts, and indicates a gradual transition from incremental modification of existing ‘open loop’ solutions, to more transformational and fully ‘closed loop’ solutions. A ‘closed loop’ solution would use a two-way elastic description of sound propagation, iteratively referring back to the field data, iteratively updating the model, and at each step iteratively constraining image gathers to be flat. And ultimately, evolving into inversion for high frequency elastic Earth parameter models, having made use of the full wavefield (including multiples and elastic mode conversion effects).

What are the main differences between incremental and transformational developments? Conventional methods, and their associated incremental developments, primarily are non-iterative over the entire workflow: some bits may be iterative (such as tomographic model update, or LS image enhancement), but the overall flow, from input data to final elastic parameters, is dealt with as a more or less a linear single-pass approach. Conversely, the transformational routes offer adaptive iteration over a larger part of the entire workflow, with the possibility of exploiting the full wavefield (multiples, conversions, etc.).

What is the motivation for moving beyond current ‘best-practice’? Ultimately, resolving a number of reservoir attributes to the extent that they can directly influence drilling decisions and further reduce risk. And, to exploit the full wavefield to the maximum extent possible (exploit multiples, elastic effects, etc., to make full use of all energy in the recorded data).

At present, the limiting assumptions we make in waveform inversion limit what we can achieve: we can currently forward model with a priori parameters for: anisotropic Vp, density, attenuation, (and perhaps Vs), but generally we invert only for P-wave anisotropic velocity. However, if we can push the frequency range of the inversion (which is very expensive), and invert for: anisotropic Vp, density, attenuation, (and perhaps Vs). Then we can directly output the desired elastic parameter volumes, rather than resorting to the intermediate step of migrated gathers which would then be used to perform very approximate reservoir parameter estimation.

What technologies are required to fulfil these ambitions? It is well known that low frequencies are required to facilitate the convergence of FWI (typically with less than perhaps 1.5 Hz). This requirement has led to a recent surge in development of low frequency (or enhanced frequency) sources (Brenders et al., 2018; Brittan et al., 2019). Long offsets are also of benefit, hence ocean bottom recordings are beneficial (Brittan et al. 2013). And, elastic modelling and associated parameter estimation will also be beneficial, and perhaps crucial for land data, were elastic effects severely affect sound propagation.

The Road Ahead

Currently, migration algorithms assume that all multiple energy (reverberation within layers) has been removed from the input data. Removing this restriction would enable us to make use of virtually

all the recorded energy in the field data. One method to achieve this goal is referred to as ‘full wavefield migration’ (FWM), outlined in Figure 3 (Verschuur and Berkhout, 2015). Adding a simultaneous update of the velocity field to this work-flow produces the ‘joint migration inversion’ scheme (JMI – Figure 3b). But ultimately, the objective would be to input the field data (with all its various arrival events) into an inversion scheme, and then directly output all requisite elastic parameters at a resolution sufficient to facilitate direct interpretation (Figure 3d): this was the goal originally envisaged by Tarantola (1984). However, this latter route is still beyond the reach of application as a routine process, but has been demonstrated in a few examples (e.g. Routh et al., 2018).

As an intermediate solution, we can employ the velocity field from FWI to better constrain conventional post-migration impedance inversion: this approach was first suggested by Cobo et al. (2019), and is outlined as a flow-chart in Figure 3c. Below we show an example of their approach (Jones et al., 2018), comparing a impedance inversion using a ‘conventional-constraint’ employing well-logs and interpreted horizons, with an FWI-only-

Figure 3. Flow chart representation of: a) full wavefield migration, b) joint migration inversion, c) impedance inversion constrained using FWI, d) full elastic FWI for direct multi-parameter estimation. (Adapted from Vershuur and Berkhout, 2015).

constraint, that uses no wells nor any picked horizons. Figure 4 shows results from Ophir’s Fortuna field (offshore Equatorial Guinea). This compares the result of a blind-test at the location of well-log which was not used to build the conventional constraints. Here, we are away from the location where the conventional result’s constraints were built, and as seen in the well-log comparison, use of the FWI constraint has resulted in a better match than the more conventional approach.

Conclusions

The evolution of FWI as a tool to improve velocity models for migration, and the move towards using such models for more direct reservoir characterization, has transformed and continues to transform the application of closed-loop’ transformational processes within exploration and production imaging projects. There remain, however, some key challenges to further successful exploitation of such methodologies: namely developing a better understanding of elastic wave propagation effects, and in obtaining the computer power to implement numerical schemes based on such an enhanced understanding.

Figure 4. In the blind-test, the FWI constrained results match the well more closely than the conventional result (courtesy of Ophir Energy and Jones et al., 2018).

Acknowledgements

The authors would like to thank the ION and Bulwark teams that have contributed to this work:  Tristram Burley, Carlos Calderón, Shihong Chi, Yannick Cobo, Paul Farmer, Juergen Fruehn, Stuart Greenwood, Claudia Hagen, Gary Martin, Ross O’Driscoll, Jeet Singh, Chao Wang and David Yingst. 

References

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