# Modified reservoir quality indicator methodology for improved hydraulic flow unit characterization using the normalized pore throat methodology (Niger Delta field as case study)

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## Abstract

The detailed characterization of complex reservoir units, typical of the thin-bedded canyon turbidites system within the clastic environment is essential for accurate reservoir modelling. The sedimentary architecture usually overprinted by late diagenesis results in the intrinsic complexities which poses major problems in modelling these systems. Although the average permeabilities exhibited by most clastic reservoirs is relatively high, the low permeabilities of the component shale strata results in low sweep efficiency and transmissibilities, and may form effective flow baffles. Recent advances in petrophysical modelling and formation evaluation studies demonstrate the applicability of normalized pore throat radius \(\overline{{R_{\text{tot}} }}\) methodology for improved reservoir characterization and production optimization in challenging systems. This paper presents a modification of the reservoir quality indicator (RQI) methodology for hydraulic flow unit characterization using the normalized pore throat concept. Result of the analysis for the various genetic reservoir units demonstrates an improvement with a correlation coefficient of 78% for the proposed modified RQI over 31% for the existing RQI method in defining the unit slope line for the Channel Storey Axis unit. In addition, regression analysis between the irreducible water saturation from mercury injection capillary pressures and FZI depicts a higher correlation coefficient of 76% for the modified RQI over 64% for the existing method. The higher correlation coefficient indicates an improved efficacy of the proposed model for hydraulic flow zone characterization. The efficacy of the proposed methodology was also validated with a numerical flow simulation model. This demonstrates improved efficient for reservoir characterization studies.

## Keywords

Normalized pore throat radius Reservoir quality index Flow zone indicators Niger Delta Flow simulation## Introduction

With the quest for hydrocarbon prospects in frontier deep water settings characterized by complex rock fabric, detailed reservoir characterization is essential for accurate field management and production optimization. The presence of multi-pore architecture within such depositional environment makes their description from petrophysics very complex. Numerous models have been reported in the literature for permeability and flow units characterization utilizing various parameters for improved petrophysical evaluation.

*ϕ*

_{ i }(v/v), contribution of the total porosity accessible at the

*i*th pressure step;

*S*

_{ wi }(v/v), incremental pore volume at the

*i*th pressure step;

*R*

_{ pi }(µm), pore throat radius at every

*i*th pressure step.

The methodology provides improved prediction of absolute permeability in uncored reservoir intervals and their implications in hydrocarbon fluid transmissibility, reservoir quality, and hydraulic flow unit definition; necessary for developing effective reservoir characterization programs.

Several authors have established various definitions of hydraulic flow units, which are the resultant of the depositional environment and diagenetic processes. The subdivision of a reservoir into flow units provides a practical means for reservoir zonation that makes use of both geological and petrophysical data representing heterogeneity observed at several scales. According to Tiab (2000), a hydraulic flow unit is a continuous body over a specific reservoir volume that practically possesses consistent petrophysical and fluid properties, which uniquely characterize its static and dynamic communication with the wellbore. There are three industry-recognized methods for calculating flow potential in clastic rocks; reservoir quality index (RQI), Winland R35 method, and Pittman methods.

This paper focuses on the modification of the reservoir quality index (RQI) approach for hydraulic flow unit characterization using the normalized pore throat method. A case study from the challenging deep water depositional environment with thin-bedded turbidite sequence is present which demonstrates the efficacy of the proposed methodology for improved reservoir characterization studies.

## The reservoir quality indicator (RQI) concept

*K*) in terms of effective porosity (

*φ*

_{e}), shape factor (

*F*

_{s}), tortuosity (

*τ*), and surface area per unit grain volume (

*S*

_{gv}) as follows:

*F*

_{s}and

*τ*were grouped into a term called Kozeny constant. However, this term actually varies among hydraulic units, although it is constant within a given rock unit.

*φ*

_{ z }), and flow zone indicator (FZI) were introduced:

As expressed in Eq. (6), the flow zone indicator *FZI* relates parameters as shape factor, tortuosity, and surface area per unit grain volume (all controlled by mineralogy and texture of the rock) to the ratio of permeability and effective porosity. This demonstrates an improved permeability modelling as a function of porosity. Thus, poorly sorted, fine-grained sands as well as rocks with high clay content typically exhibit high surface area, high shape factor, and tortuosity and hence, low FZI values. In contrast, rock samples composed of coarse grained and well-sorted grains have lower surface areas, lower shape factor, and tortuosity and consequently, higher FZI values.

## Integrating the \(\overline{{R_{{{\text{tot}} }} }}\) with the RQI concept

Onuh et al. (2015a, b) defined the relationship for pseudo averages of normalized pore throat radius as: \(R_{{{\text{tot}} }} = \overline{{R_{{{\text{tot}} }} }} \varphi_{n}\); where \(\overline{{R_{{{\text{tot}} }} }}\), genetic units averages of pore throat radius for the various depositional environments within the Niger Delta system; \(\varphi_{n} = 0.3\left( {\frac{1 - \varphi }{\varphi }} \right)\).

*R*

_{tot}in the RQI formulation (Eq. 4) results in:

The plot of the logarithm of RQI versus the logarithm of *φ* _{ z } rearranges the porosity–permeability relationship and provides trends for various rock units with similar depositional/diagenetic imprints. Then, samples that lie on the same line have similar hydraulic behaviour, and thereby, similar fluid-flow characteristics. These trends can be fitted with parallel straight lines with unit slope whose intercept at *φ* _{ z } = 1 defines the FZI of each distinctive hydraulic unit.

## Case study of the clastic turbidite reservoirs

### Introduction

*T*

_{1}and

*T*

_{2}spectrum confirms the zones as highly prolific with good reservoir and fluid properties. Special as well as routine core analysis dataset were obtained from the interval: X,226–X,567 ft MD.

Genetic Units Classification for the Turbidite/Deep Water Environment developed for the study

Generalized classification | Units | Genetic reservoir units |
---|---|---|

Amalgamated channel fill Sandstone | 1 | Channel lag |

Channel Storey Axis (CSA) | ||

2 | Channel Storey Margin (CSM) | |

Isolated and sinuous channel Complexes | 3 | Inter-channel thin beds (ICTB) |

Mud-rich thin beds (MRTB) | ||

Levee/overbank/mudstone | 4 | Levees/overbank |

Marine mudstone |

### Permeability modelling using the \(\overline{{R_{{{\text{tot}} }} }}\) methodology

*T*

_{2}distribution; detailed geologic core description, stress-corrected porosities, and air permeabilities were available from routine core analysis. Seventy-five measured core analysis dataset covering two (2) prolific canyon reservoirs were obtained for validating the predicted permeabilities. The measured core permeabilities were analysed using nitrogen gas under a sleeve pressure of 400 psig permeameter and was corrected for the Klinkenberg effects. The predicted permeabilities were also compared to NMR-based correlations after the Schlumberger Research Doll (SDR) and Coates methodologies. Track 5 of Fig. 5 presents a plot of corrected core permeabilities with reference to the predicted from the proposed \(\overline{{R_{{{\text{tot}} }} }}\), SDR and Coates models. The proposed approach demonstrates good correlation over the existing methodologies with a correlation coefficient of 0.987 and RMSE of 0.092.

### Reservoir zonation using the \(\overline{{R_{\text{tot }} }}\)-based modified RQI methodology

The modified RQI concept based on a combination of \(\overline{{R_{{{\text{tot}} }} }}\) and permeability data demonstrates efficacy for hydraulic flow unit delineation. A simple summation and normalization of the modified RQI is sufficient for transforming rock-type-based reservoir zonation into petrophysical-based flow units for input into a numerical flow simulator that honours the foot-by-foot characteristics at the wellbore. In such a plot, consistent zones are characterized by straight lines with the slope of the line indicating the overall reservoir quality within a particular depth interval. The lower the slope of the normalized modified RQI lines delineating each zone, the better the reservoir.

*n*, total number of data;

*i*, number of individual data points.

### Implication of the \(\overline{{R_{{{\text{tot}} }} }}\)-based modified RQI methodology in 3D simulation studies

Reservoir and fluid properties

Property | Value |
---|---|

Datum depth | 4600 ftss |

Initial reservoir pressure, Pi | 2010 psia |

Bubble point pressure, Pb | 1998 psia |

FBHP (06, 16) | 1912 psia |

Reservoir oil density | 0.61 g/cc |

Reservoir oil viscosity | 0.5 cp |

Proven oil column | 170 ft |

Boi @ Pi | 1.636 rb/stb |

Initial solution GOR, Rsi | 298 scf/stb |

Reservoir temperature | 167 °F |

Stock tank oil density | 28.9 °API |

Gas gravity (air = 1) | 0.89 |

Rock compressibility | 3.00E−06 |

Average porosity | 0.28 v/v |

Average water saturation, Sw | 0.2 v/v |

Average permeability, K | 3100 mD |

STOIIP | 201.0 MMSTB |

Np (06/16) | 84.3 MMSTB |

RF (06/16) | 41.9% |

3D reservoir model dimensions

Model dimensions | |||
---|---|---|---|

Axis | Min | Max | Delta |

| 481,003 | 491,937.8 | 10,934.82 |

| 54,392.98 | 60,340.74 | 5947.76 |

Depth | −4670 | −4120 | 550 |

Cells (NX × NY × NZ) | 98 × 46 × 187 | ||

Total number of 3D cells | 842,996 | ||

Number of faults | 0 | ||

Number of zones | 7 | ||

Number of segments | 1 |

A numerical flow simulation was performed by integrating all requisite reservoir and fluid data to demonstrate model reliability for future reservoir management practices. Two model cases were established with only the permeability models (PERM-X, -Y, -Z) as variables to validate the applicability of the proposed methodology for permeability modelling and hydraulic flow unitization. The proposed \(\overline{{R_{{{\text{tot}} }} }}\) permeability model and the Timur–Coates methods were implemented. Both models were constrained to RESV for material/energy balance (pressure match) and ORAT for saturation match.

## Conclusions

- 1.
The modified reservoir quality index methodology using the normalized pore throat radius concept demonstrates improved capabilities for petrophysical evaluation.

- 2.
The cumulative normalized RQI presents potentials for reservoir flow units’ definition which bears strong correlation between rock-type-based reservoir zonation and petrophysical-based flow units.

- 3.
The proposed model demonstrates an improved capability in aligning all samples within same genetic reservoir units on same trend/straight line than existing RQI method (Ameafule et al.). This presents an improved hydraulic flow zone delineation method using the RQI concept.

- 4.
The proposed model possesses the capability of modelling extreme value of connections. This results in improved prediction of permeability and permeability distributions from wireline logs in partially cored/uncored intervals and adjacent wells for improved completions and enhanced recovery decisions.

- 5.
The proposed method outperforms existing works of Timur–Coates, and NMR-based SDR, and Coates methods. This depicts potentials for efficient reservoir modelling.

## Notes

### Acknowledgements

The authors would like to thank the management of the Department of Petroleum Resources (DPR), and the Petroleum Technology Development Fund (PTDF), Nigeria, for their support and permission to publish this article. Special thanks and appreciation to the SPDC petrophysics and geosolution team who were involved in discussions that made this possible and to Schlumberger for their Techlog petrophysical and statistical tool, PETREL and ECLIPSE reservoir simulators tools.

## References

- Amaefule JO, Altunbay M, Tiab D, Kersey DG, Keelan DK (1993) Enhanced reservoir description: using core and log data to identify hydraulic (flow) units and predict permeability in uncored intervals/wells. SPE 26436, presented at 68th annual. Technical conference and exhibition. Houston, TXGoogle Scholar
- Carmen PC (1937) Fluid Flow through Granular Beds. Trans, AIChE 15:150Google Scholar
- Kozeny J (1927) Uber Kapillare Leitung des Wassers im Boden, Stizurgsberichte. Royal Academy of Science, Vienna, Proc Class I 136:271Google Scholar
- Onuh HM, Ogbe DO, Nwosu C (2015a) Genetic units based permeability prediction for clastic reservoirs using normalized pore throat radius (Niger Delta as case study). Int J Pet Eng IJPE 1(4)Google Scholar
- Onuh HM, Arinkoola OA, David OO (2015b) Genetic unit averages of pseudo-normalized pore throat radius for improved permeability predictions (Niger Delta as case study). J Pet Explor Prod Technol 5(2):147–155CrossRefGoogle Scholar
- Tiab D (2000) Advances in petrophysics, vol 1—flow units. Lecture notes manual, University of OklahomaGoogle Scholar

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