Petrophysical core-based zonation of OW oilfield in the Bredasdorp Basin South Africa

This study aims to generate rock units based on core permeability and porosity of OW oilfield in the Bredasdorp Basin offshore South Africa. In this study, we identified and classified lithofacies based on sedimentology reports in conjunction with well logs. Lucia's petrophysical classification method is used to classify rocks into three classes. Results revealed three lithofacies as A (sandstone, coarse to medium-grained), B (fine to medium-grained sandstone), and C (carbonaceous claystone, finely laminated with siltstone). Lithofacies A is the best reservoir quality and corresponds to class 1, while lithofacies B and C correspond to class 2 and 3, which are good and poor reservoir quality rock, respectively. An integrated reservoir zonation for the rocks is based on four different zonation methods (Flow Zone indicator (FZI), Winland r35, Hydraulic conductivity (HC), and Stratigraphy modified Lorenz plot (SMLP)). Four flow zones Reservoir rock types (RRTs) were identified as RRT1, RRT3, RRT4, and RRT5, respectively. The RRT5 is the best reservoir quality composed of a megaporous rock unit, with an average FZI value between 5 and 10 µm, and HC from 40 to 120 mD/v3, ranked as very good. The most prolific flow units (RRT5 and RRT4 zones) form more than 75% of each well's flow capacities are supplied by two flow units (FU1 and FU3). The RRT1 is the most reduced rock quality composed of impervious and nanoporous rock. Quartz is the dominant framework grain, and siderite is the dominant cement that affects flow zones. This study has demonstrated a robust approach to delineate flow units in the OW oilfield. We have developed a useful regional petrophysical reservoir rock flow zonation model for clastic reservoir sediments. This study has produced, for the first time, insights into the petrophysical properties of the OW oilfield from the Bredasdorp Basin South Africa, based on integration of core and mineralogy data. A novel sandstone reservoir zonation classification criteria developed from this study can be applied to other datasets of sandstone reservoirs with confidence.

Prediction of Hydraulic Flow in the New Zohr Carbonate Reservoir, Eastern Mediterranean Using Artificial Neural Networks

A new gas reservoir includes the carbonates of upper-Cretaceous Formation in the Zohr oilfield of eastern Mediterranean Sea in Egypt. The main aim of this study is to assess the new carbonate reservoir by thin section study and estimate hydraulic flow units HFUs by smart system. This carbonate formation is now considered the most important gas reservoir in northern Egypt. In this paper five microfacies were identified based on microscope petrographic analysis. The examined rocks were formed in lagoon, shoal and open marine depositional environments. The relationships between microfacies and flow units are further evaluated in this study. The determination of such relationships have proven to be challenging due to petrographic complications arising from diagenetic processes. The correlation behind pore space percentage and permeability is important to recognize hydraulic flow in the reservoir under consideration in this study.

Fracture Characterization in Deep Gas Reservoirs to Identify Fracture Enhanced Flow Units, Offshore Abu Dhabi

High gas flow rates in deep-buried dolomitized reservoir from an offshore field Abu Dhabi cannot be explained by the low matrix permeability. Previous permeability multiplier based on distance to major faults is not a solid geological solution due to over-simplifying reservoir geomechanics, overlooking folding-related fractures, and lack of detailed fault interpretation from poor seismic. Alternatively, to characterize the heterogeneous flow related with natural fractures in this undeveloped reservoir, fracture network is modelled based on core, bore hole imager (BHI), conventional logs, seismic data and test information. Limited by investigation scale, vertical wells record apparent BHI, and raw fracture interpretation cannot represent true 3D percolation reflected on PLT. To overcome this shortfall, correction based on geomechanics and mechanical layer (ML) analysis is performed. Young's modulus (E), Poisson ratio (ν), and brittleness index are calculated from logs, describing reservoir tendency of fracturing. Other than defining MLs, bedding plane intensity from BHI is also used as an indicator of fracture occurrence, since stress tends to release at strata discontinuity and forms bed-bounded fractures observed from cores. Subsequently, a new fracture intensity is generated from combined geomechanics properties and statistics average of BHI-derived fracture occurrence within the ML frame, which improves match with PLT and distinguishes fracture enhance flow intervals consistently in all wells. Seismic discontinuity attributes are used as static fracture footprints to distribute fractures from wells to 3D. The final hybrid DFN comprises large-scale deterministic zone-crossing fractures and small-scale stochastic bed-bounded fractures. Sub-vertical open fractures are dominated by NE-SW wrenching fractures related with Zagros compression and reactive salt upward movement. There is no angle rotation of fractures in different fault blocks. Open fractures in other strikes are supported by partial cements and mismatching fracture walls on computerized tomography (CT) images. ML correlation shows vertical consistence across stratigraphic framework and its intensity indicates fracture potential of vertical zones reflected by tests. Fracture-enhanced flow units are further constrained by a threshold in both combined geomechanics properties and statistics average of raw BHI fracture intensity in ML frame. As a result, final fracture network maps reservoir brittleness and flow potential both vertically and laterally, identifying fracture regions along folding axis not just major faults, evidenced by wells and seismic. According to the upscaling results, the case study reveals a type-III fractured reservoir, where fractures contribute to flow not to volume. Fracture network enhances bed-wise horizontal communication but also opens vertical feeding channels. Fracture permeability is mainly influenced by aperture and intensity, while aspect ratio, fracture length, and proportion of strikes and dips mainly influence permeability distribution rather than absolute values. This study provides a production-oriented characterization workflow of natural fracture heterogeneity based on correction of raw BHI in undeveloped fields.

4D Seismic Detectability in a Brazilian Pre-Salt Carbonate Field

This paper is dedicated to a pre-salt carbonate field located within the Santos Basin, Brazil, comprising thick Aptian reservoirs interspersed with igneous rocks. One of the main challenges for reservoir management is the surface constraint on the gas, as all of the produced gas will have to be reinjected and can be miscible with the in-situ hydrocarbons. The recovery mechanism selected is mainly WAG (water alternating gas) injection, with both producers and injectors equipped with intelligent completions using Inflow Control Valves (ICVs). A 4D seismic monitoring survey is planned to delineate gas and water fronts in reservoir flow units about 10m thick, providing critical information to help piloting a planned 6-month WAG cycle for improved recovery. Seismic imaging is challenging in this case and 4D signal is expected to be weak (±2% dIp/Ip). We propose here, a methodology, based on a 1-D Gassmann fluid substitution model at wells (only limited reservoir fluid PVT data available) to rapidly answer the following pertinent questions as posed by the asset team in charge of the field: From a phenomenological stand-point and neglecting some possible processing, imaging and acquisition challenges, will 4D data (post 4D inversion) detect a gas streak from an injector to a producer? What is the 4D seismic detection limit based on reservoir thickness? What kind of seismic acquisition will assure this detectability? Under the assumptions made in this work, this methodology shows that a permanent system of acquisition seems to be a fit-for-purpose technology for detectability. Further work is however recommended using full complement of a 3D static and dynamic simulation model coupled with a complete fluid PVT model in order to assess more complex 3D dynamic interactions between the injectors and producers.

Artificial Intelligence Method of Flow Unit Division Based on Waveform Clustering: A Case Study on Zhetybay Oil Field, South Mangyshalk Basin, Kazakhstan

The Zhetybay Field is located in the South Mangyshlak Sub-basin, a delta front sedimentary reservoir onshore western Kazakhstan. It was discovered in 1961 and first produced by waterflooding in 1967. After more than 50 years of waterflooding development, the reservoirs are generally in the mid-to-high waterflooded stage and oil-water distribution becomes complicated and chaotic. It is very difficult to handle and identify so much logging data by hand since the oilfield has the characteristics of high-density well pattern and contains rich logging information with more than 2000 wells. The wave clustering method is used to divide the sedimentary rhythm of the logging curve. Sedimentary microfacies manifested as a regression sequence, with four types of composite sand bodies including the composite estuary bar and distributary channel combination, the estuary bar connected to the dam edge and the distributing channel combination, the isolated estuary bar and distributing channel combination, and the isolated beach sand. In order to distinguish the flow units, the artificial intelligence algorithm-support vector machine (SVM) method is established by learning the non-linear relationship between flow unit categories and parameters based on developing flow index and reservoir quality factor, summarizing permeability logarithm and porosity degree parameters in the sedimentary facies, and analyzing the production dynamic. The flow units in Zhetybay oilfield were classified into 4 types: A, B1, B2 and B3, and the latter three are the main types. Type A is distributed in the river, type B1 is distributed in the main body of the dam, type B2 is mainly distributed in the main body of the dam, and some of B2 is distributed in the dam edge, and B3 is located in the dam edge, sheet sand and beach sand. The results show that the accuracy of flow unit division by support vector machines reaches 91.1%, which clarifies the distribution law of flow units for oilfield development. This study is one of the significant keys for locating new wells and optimizing the workovers to increase recoverable reserves. It provides an effective guidance for efficient waterflooding in this oilfield.

Characterization of flow units, rock and pore types for Mishrif Reservoir in West Qurna oilfield, Southern Iraq by using lithofacies data

This study has been accomplished by testing three different models to determine rocks type, pore throat radius, and flow units for Mishrif Formation in West Qurna oilfield in Southern Iraq based on Mishrif full diameter cores from 20 wells. The three models that were used in this study were Lucia rocks type classification, Winland plot was utilized to determine the pore throat radius depending on the mercury injection test (r35), and (FZI) concepts to identify flow units which enabled us to recognize the differences between Mishrif units in these three categories. The study of pore characteristics is very significant in reservoir evaluation. It controls the storage mechanism and reservoir fluid properties of the permeable units while pore structure is a critical controlling factor for the petrophysical properties and multiphase-flow characteristics in reservoir rocks. Flow zone indicator (FZI) has been used to identify the hydraulic flow units approach (HFUs). Each (HFU) was reproduced by certain FZI and was supposed to have similar geological and petrophysical properties. The samples were from four lithofacies, mA, CRII, mB1, and mB2. Because of the wide range of cored-wells samples (20 wells), this paper is updated the previous studies and indicated some differences in the resulting categories. It was noticed as results of this study that the rocks types of the lower Mishrif were mostly ranged from wackestone to packstone in the upper part of mB2 which reflected mid-ramp facies while the upper part of mB2 referred to shoal facies and for the mB1 unit the rocks types mostly range from packstone to grainstone with some points as wackestone marked as shoal and rudist bioherm facies. Grainstone relatively decreases with the increasing of depth from upper to lower Mishrif while wackestone and packstone indicated increasing in the same direction. The unit mA is marked as mesopores and macropores, while megapores and macropores feature increased in mB1 which has been noticed in the northern part of West Qurna oilfield due to increasing shoal and rudist bioherm facies, the mB2 unit revealed increasing in mesoporous and decreasing in megaporous. The upper Mishrif (mA) has three flow units, while the lower Mishrif (mB1, mB2) has eight flow units four for each reservoir unit.