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.

Study of Petrophysical Properties of the Yamama Formation in Siba Oilfield

Evaluating a reservoir to looking for hydrocarbon bearing zones, by determining the petrophysical properties in two wells of the Yamama Formation in Siba field using Schlumberger Techlog software. Three porosity logs were used to identify lithology using MN and MID cross plots. Shale volume were calculated using gamma ray log in well Sb-6ST1 and corrected gamma ray in well Sb-5B. Sonic log was used to calculate porosity in bad hole intervals while from density log at in-gauge intervals. Moreover, water saturation was computed from the modified Simandoux equation and compared to the Archie equation. Finally, Permeability was estimated using a flow zone indicator. The results show that the Yamama Formation is found to be mainly limestone that confirmed by cuttings description and this lithology intermixed with some dolomite, in addition to gas and secondary porosity effects. Generally, the formation is considered clean due to the low shale volume in both wells with the elimination of the uranium effect in well Sb-5B. The calculated porosity was validated by core porosity in YC and YD units. Modified Simandoux gives a better estimation than the Archie equation since it takes into account the conductive of matrix in addition to the fluid conductivity. Five equations were obtained from porosity permeability relationship of core data based on five hydraulic flow units reorganized from the cross plot of reservoir quality index against normalized porosity index. The overall interpretation showed that YC and YD units are the best quality hydrocarbon units in the Yamama Formation, while YA came in the second importance and has properties better than YB. Moreover, YE and YFG are poor units due to high water saturation.

Predicting the flow zone indicator of carbonate reservoirs using NMR echo transforms, and routine open-hole log measurements: Insights from a field case study spanning extreme micro-structure properties

Predicting the flow zone indicator is essential for identifying the hydraulic flow units of hydrocarbon reservoirs. Delineation of hydraulic flow units is crucial for mapping petrophysical and rock mechanical properties. Precise prediction of the flow zone indicator (FZI) of carbonate rocks using well log measurements in un-cored intervals is still a daunting challenge for petrophysicists. This study presents a data mining methodology for predicting the rock FZI using NMR echo transforms, and conventional open-hole log measurements. The methodology is applied on a carbonate reservoir with extreme microstructure properties, from an oil “M” field characterized by a relatively high-permeability with a median of approximately 167 mD, and a maximum of 3480 mD. The reservoir from the M field features detritic, or vuggy structure, covering a wide range of rock fabrics varying from microcrystalline mudstones to coarse-grained grainstones. Porosity has a median of approximately 22%. Dimensional analysis and regression analysis are applied for the derivation of four transforms that appear to capture approximately 80% of the FZI variance. These four transforms are formulated using the geometric mean of the transverse NMR relaxation time (T2lm), the ratio of the free fluid index (FFI) to the bulk volume irreducible (BVI), the bulk density, the sonic compressional travel time, the true resistivity, the photo-electric absorption, and the effective porosity. Non-linear regression models have been developed for predicting the FZI using the derived transforms, for the carbonate reservoir from the M field. The average relative error for the estimated FZI values is approximately 52%. The same transforms are used as input for training a developed general regression neural network (GRNN), built for the purpose of predicting rock FZI. The constructed GRNN predicts FZI with a notable precision. The average absolute relative error on FZI for the training set is approximately 3.1%. The average absolute relative error on FZI for the blind testing set is approximately 22.0 %. The data mining approach presented in this study appears to suggest that (i) the relationship between the flow zone indicator and open-hole log attributes is highly non-linear, (ii) the FZI is highly affected by parameters that reflect rock texture, rock micro-structure geometry, and diagenetic alterations, and (iii) the derived transforms provide a means for further enhancement of the flow zone indicator prediction in carbonate reservoirs.

Integrating XRD and Well Logging Data to Establish Electro-Facies and Permeability Models for an Unconventional Heterogeneous Tight Gas Reservoir, Obaiyed Giant Gas Field

Formation evaluation in heterogeneous reservoirs can be very challenging especially in fields that extend over several kilometers in area where the permeability varies from 0.1 mD up to 1000 D within the same porosity. The porosity, hydrocarbon saturation and net sand thickness in most of Obaiyed field wells are consistent; hence, the productivity of these wells is enormously dependent on the reservoir permeability. Since the permeability is highly heterogeneous, initial production rate of the wells varies between few MMSCFD to almost one hundred MMSCFD. The huge permeability variation led to a tremendous uncertainty in the dynamic modeling, which resulted in an inaccurate production forecast affecting the field economics estimation. Understanding permeability distribution and heterogeneity in Obaiyed field is the key factor for establishing a realistic permeability model, which will lead to a successful field development strategy. Extensive work was performed to understand key factors that govern the permeability in Obaiyed using the data of 1-kilometer length of cores acquired in more than 50 wells covering different reservoir properties in the field. Core data were used to separate the reservoir into different Hydraulic Flow Units (HFU) according to Amaefule's work performed on the Kozeny-Carmen model. Afterwards, a correlation between the HFU and well logs was established using IPSOM Electro-Facies module in order to define the flow units in un-cored wells. The result of this correlation was used to calibrate a Porosity-Permeability relationship for each flow unit. The next step was examining the clay-type distribution and diagenesis in each flow unit using the petrographic analysis (XRD) results from the core xdata. All factors controlling the permeability can now be represented in hydraulic flow units which are considered as a method of measurement of the reservoir quality. Consequently, property maps were constructed showing the location and continuity of each of the flow units, leading to a more deterministic approach in the well placement process. Based on this new work methodology, a production cut-off criteria relating the reservoir productivity to both clay minerals presence and percentages was established for multiple wells scenarios. As a result, the development strategy of the field changed from only vertical wells to include horizontal wells as well which proved to be the only economic approach to produce the Illite dominated zones. This paper presents a workflow to provide a representative estimation of permeability in extremely heterogeneous reservoirs especially the ones dominated by complex clay distribution.

Geophysical evaluation of geohydrokinetic properties of aquifer units in parts of Enugu state, Nigeria

Vertical electrical sounding employing schlumberger electrode configuration was carried out in thirty locations across some parts of Enugu state, to investigate the hydrokinetic properties of hydrogeologic units of the study area. The result shows that resistivity and thickness of aquifer ranges from 27.3 to 59,569.0 Ωm and 23.3 to 242.1 m respectively. Permeability and fractional porosity values range from 4,531.254 to 74,006.76 mD and 0.026 to 0.159. AQI having a mean value of 13.5451 μm range from 6.809 to 52.976 μm. FZI and HFU values range from 37.582 to 1,962.074 μm and 18 to 26 respectively. Contour maps were generated from the results to visualize the variations of the hydrokinetic properties across the study area. From the contour maps, southern part of the study area was identified to be characterized with high AQI, FZI and HFU with northwestern part and a small proportion along the southwestern part identified as areas with low AQI, FZI and HFU. HFU along the study area was observed to be fractionated into nine distinct properties (HFU 18, HFU 19, HFU 20, HFU 21, HFU 22, HFU 23, HFU 24, HFU 25, and HFU 26) with HFU 19 and HFU 20 dominating the area. The results from the nine hydraulic flow units based on flow zone indicator cut off values (Log FZI>0.25) shows that the reservoir quality is very high.

Rock typing based on hydraulic and electric flow units for reservoir characterization of Nubia Sandstone, southwest Sinai, Egypt

The present work describes and evaluates the reservoir quality of the sandstone of the Nubia Formation at the Gebel Abu Hasswa outcrop in southwest Sinai, Egypt. Hydraulic flow unit (HFU) and electrical flow unit (EFU) concepts are implied to achieve this purpose. The Paleozoic section made up of four formations has been studied. The oldest is Araba Formation followed by Naqus formations (Nubia C and D) overlay by Abu Durba, Ahemir and Qiseib formations (Nubia B), where the Lower Cretaceous (Nubia A) is represented by the Malha Formation. The studied samples have been collected from Araba, Abu Durba, Ahemir and the Malha formations. The hydraulic flow unit (HFU) discrimination was carried out based on permeability and porosity relationship, whereas the electrical flow unit (EFU) differentiation was carried out based on the relationship between formation resistivity factor and porosity. Petrographic investigation of the studied thin sections illustrates that the studied samples are mainly quartz arenite. Important roles to enhance or reduce the pore size and/or pore throats controlling the reservoir petrophysical behavior are due to the diagenetic processes. The present study used the reservoir quality index (RQI) and Winland R35 as additional parameters applied to discriminate the HFUs. The study samples have five hydraulic flow units of different rock types, where the detected electrical flow units are only three. The differences between them are may be due to the cementation process with iron oxides that might act as pore filling, lining and pore bridging, sometimes bridges helping to decrease permeability without serious reduction in porosity. The reduction between the number of EFUs and HFUs comes from the effect of diagenesis processes which is responsible for a precipitation of different cement types such as different clay minerals and iron oxides.

Hydraulic flow unit classification from core data: case study of the Z gas reservoir, Poland

Permeability and porosity are essential parameters for estimating hydrocarbon production from reservoir rocks. They are combined in an additional factor, the Flow Zone Index (FZI), which is the basis for defining the hydraulic flow unit (HFU). Each HFU is a homogeneous section of a reservoir rock with stable parameters that allow for media flow. Hydraulic flow units are determined from the porosity and permeability of core or well logs. The simple statistical methods are applied for HFU classification and then improve permeability prediction. This paper also shows how to quickly apply the global hydraulic elements (GHE) method for HFU classification. The methodology is tested on the Miocene formation of a deltaic facies from the Carpathian Foredeep in South-Eastern Poland.

Cumulative oil production in flow unit-crossing wells estimated by multilayer perceptron networks

Knowing the ultimate oil production in wells is a crucial point for reservoir planning and management to anticipate value for money. Commercial reservoir simulators are able to predict production curves with high confidence, but repetitive tasks in a few cases may spend a precious time of staff as well as require a large computational effort. Although artificial intelligence (AI) is providing an alternative path to the usual workflow, many commercial simulators lack robust AI algorithms. This work introduces a methodology based on a multilayer perceptron (MLP) neural network to predict the final cumulative oil production of a reservoir at vertical wells that cross hydraulic flow units (HFUs), which are volumes endowed with good flow attributes. Each well location is attached to special spots previously determined from clustering and calculation of maximum closeness centrality points (MaxCs) within a class of HFUs. The database is divided into training, validation, and testing sets organized after processing the UNISIM-I-D synthetic model, representative of the Namorado Field, Campos Basin, Brazil. The key rationale of this paper is to use the feature of MaxCs of being drivers for well placement as knowledge base to learn the production mechanisms of the oilfield. The outcomes are presented from two perspectives: an original MLP and its post-processed version. Both are compared with reservoir simulations carried out in CMG Imex$$^{\copyright }$$ © and achieve reasonable agreement. The performance is measured by root-mean-squared error (RMSE) and mean absolute scaled error (MASE) both in original and post-processed versions. We show that average RMSE and MASE values near 0.07 and 14.00, respectively, are achieved without post-processing. With post-processing, gains of up to 43% are reported for the integral oil volume.