Evaluation of Source Rock Quality in Chang-7 Member of Major Oil Fields in Ordos Basin

Petroleum is an important natural resource for human. The demand for petroleum in the next several decades will continue to be high, which requires the exploitation of more petroleum sources. Chang-7 member of the Ordos basin is proven to be the richest among all members, and this research took a focus on making a comparison between the source rock quality of Chang-7 member in four major oil fields in the basin, to provide a simple view of priority oil fields for exploitation in the future. The research analyzed the source rock quality of four oil fields from the TOC, A%, rock type, R0, sedimentary environment, and kerogen type, and found a superiority of source rock quality of Maling and Zhidan oil field in multiple dimensions, mainly triggered by their sedimentary environment. The research concluded that the oil fields that was once in the central-southern region of the basin contains a slightly better source rock quality among the chosen fields, which is related to a more stable sedimentary environment in the early Triassic.

Wettability of Carbonate Reservoir Rocks: A Comparative Analysis

Various methods have been proposed for the evaluation of reservoir rock wettability. Among them, Amott–Harvey and USBM are the most commonly used approaches in industry. Some other methods, such as the Lak and modified Lak indices, the normalized water fractional flow curve, Craig’s triple rules of thumb, and the modified Craig’s second rule are based on relative permeability data. In this study, a set of capillary pressure curves and relative permeability experiments was conducted on 19 core plug samples from a carbonate reservoir to evaluate and compare different quantitative and qualitative wettability indicators. We found that the results of relative permeability-based approaches were consistent with those of Amott–Harvey and USBM methods. We also investigated the relationship between wettability indices and rock quality indicators RQI, FZI, and Winland R35. Results showed that as the rock quality indicators increased, the samples became more oil-wet.

Utilizing NMR Workflow to Optimize Power Water Injector Placement in the Presence of Tar Barriers

The objective of this work is to optimize the placement of horizontal power water injector (PWI) wells in stratified heterogeneous carbonate reservoir with tar barriers. The key to successful reservoir navigation is a reliable real-time petrophysical analysis that resolves rock quality variations and differentiates tar barriers from lighter hydrocarbon intervals. An integrated workflow has been generated based on logging-while drilling (LWD) triple combo and Nuclear Magnetic Resonance (NMR) logging data for fluid identification, tar characterization and permeability prediction. The workflow has three steps; it starts with the determination of total porosity using density and neutron logs, the calculation of water-filled porosity from resistivity measurements and an additional partitioning of porosity into bound and free fluid volumes using the NMR data. Second, the total and water-filled porosity, the NMR bound fluid and NMR total porosity are used as inputs in a hydrocarbon compositional and viscosity analysis of hydrocarbon-bearing zones for the recognition of tar-bearing and lighter hydrocarbon intervals. Third, in the lighter hydrocarbon intervals, NMR logs are further analyzed using a multi-cutoff spectral analysis to identify microporous and macroporous zones and to calculate the NMR mobility index. The ideal geosteering targets are highly macroporous rocks containing no heavy hydrocarbons. In horizontal wells, the method is validated using formation pressure while drilling (FPWD) measurements. The procedure has been utilized in several wells. The original well path of the first injector was planned to maintain a safe distance above an anticipated tar-bearing zone. Utilizing the new real-time viscosity evaluation, the well was steered closer to the tar zone several feet below the original plan, setting an improved well placement protocol for subsequent injectors. In the water- or lighter hydrocarbon-bearing zones, spectral analysis of NMR logs clearly accentuated micro- and macroporous carbonate intervals. The correlation between pore size and rock quality has been corroborated by FPWD mobility measurements. In one well, an extremely slow NMR relaxation may indicate wettability alteration in a macroporous interval. An integrated real-time evaluation of porosity, fluid saturation, hydrocarbon viscosity and pore size has enhanced well placement in a heterogeneous carbonate formation where tar barriers are also present. The approach increased well performance and substantially improved reservoir understanding.

Characterization of Tight Carbonate Reservoir by Using Nuclear Magnetic Resonance Log Analysis in the Bahrain Field

The Bahrain Field (the "Field"), discovered in 1932, is an asymmetric anticline trending in a North-South direction of the Kingdom of Bahrain. It is a geologically complex field with 16 multi-stack carbonate and sandstone reservoirs, most of them oil bearing. The fluids varying from shallow tarry oil in Aruma to dry gas in the Khuff and pre-Khuff reservoirs. The Field has more than 2000 wells of which 90% have good quality log data. The Ostracod and Magwa reservoirs are heterogeneous, layered tight reservoirs and have been on production since 1964. The Ostracod reservoir consists of very heterogeneous with limestone intervals intercalated between shale layers, with a total thickness of around 200 ft. The Magwa reservoir conformably underlies the Ostracod reservoir. The Ostracod averages 120 ft in thickness and is dominated by limestone with high porosity, low permeability, and variable water saturations. Core derived permeability measurements are usually less than 5 mD and porosities average 22%. Production performance of individual wells is extremely variable and in many cases appears to be at odds with log-calculated saturations. Wells having good oil saturation often produce water and wells with low oil saturation produce high volumes of oil. Several studies have been conducted in an attempt to understand and resolve this. The variability of oil saturation which has been mapped both laterally across the Field and vertically within wells, led to the question of what caused the variation in oil saturation. The variation is not a function of depth, which one might expect. Causes might include oil failure to migrate into certain reservoir compartments, a loss of the original charge to shallower reservoir or the oil charge been restricted by rock quality. This paper attempts to address the variability in saturations seen across the Field and link known productivity to the Petrophysical interpretations. Nuclear Magnetic Resonance (NMR) logs had been employed in a targeted area of the Field in order to investigate rock quality in an attempt to explain the oil saturation distribution. A small NMR core study was undertaken in order to calibrate the NMR log response. The NMR data had been initially processed with what was considered a representative cut-off for Middle East Carbaonte rocks. This core study resulted in a surprisingly low series of T2 cut-off. The NMR logs were reprocessed with the more representative T2 cut-off. The resulting bound and free fluid fractions seemed to explain the observed well production.

Tight Jurassic Carbonate Reservoir Characterization and Fluid Typing Identification by Integrating Magnetic Resonance, Elemental Spectroscopy and Micro-Resistivity Image Data in Umm Ross Field, West Kuwait, Case Study

The Middle Marrat reservoir of Jurassic age is a tight carbonate reservoir with vertical and horizontal heterogeneous properties. The variation in lithology, vertical and horizontal facies distribution lead to complicated reservoir characterization which lead to unexpected production behavior between wells in the same reservoir. Marrat reservoir characterization by conventional logging tools is a challenging task because of its low clay content and high-resistivity responses. The low clay content in Marrat reservoirs gives low gamma ray counts, which makes reservoir layer identification difficult. Additionally, high resistivity responses in the pay zones, coupled with the tight layering make production sweet spot identification challenging. To overcome these challenges, integration of data from advanced logging tools like Sidewall Magnetic Resonance (SMR), Geochemical Spectroscopy Tool (GST) and Electrical Borehole Image (EBI) supplied a definitive reservoir characterization and fluid typing of this Tight Jurassic Carbonate (Marrat formation). The Sidewall Magnetic resonance (SMR) tool multi wait time enabled T2 polarization to differentiate between moveable water and hydrocarbons. After acquisition, the standard deliverables were porosity, the effective porosity ratio, and the permeability index to evaluate the rock qualities. Porosity was divided into clay-bound water (CBW), bulk-volume irreducible (BVI) and bulk-volume moveable (BVM). Rock quality was interpreted and classified based on effective porosity and permeability index ratios. The ratio where a steeper gradient was interpreted as high flow zones, a gentle gradient as low flow zones, and a flat gradient was considered as tight baffle zones. SMR logging proved to be essential for the proper reservoir characterization and to support critical decisions on well completion design. Fundamental rock quality and permeability profile were supplied by SMR. Oil saturation was identified by applying 2D-NMR methods, T1/T2 vs. T2 and Diffusion vs. T2 maps in a challenging oil-based mud environment. The Electrical Borehole imaging (EBI) was used to identify fracture types and establish fracture density. Additionally, the impact of fractures to enhance porosity and permeability was possible. The Geochemical Spectroscopy Tool (GST) for the precise determination of formation chemistry, mineralogy, and lithology, as well as the identification of total organic carbon (TOC). The integration of the EBI, GST and SMR datasets provided sweet spots identification and perforation interval selection candidates, which the producer used to bring wells onto production.

Automation of Carbonate Rock Thin Section Description Using Cognitive Image Recognition

Simulation Engineers and Geomodelers rely on reservoir rock geological descriptions to help identify baffles, barriers and pathways to fluid flow critical to accurate reservoir performance predictions. Part of the reservoir modelling process involves Petrographers laboriously describing rock thin sections to interpret the depositional environment and diagenetic processes controlling rock quality, which along with pressure differences, controls fluid movement and influences ultimate oil recovery. Supervised Machine Learning and a rock fabric labelled data set was used to train a neural net to recognize Modified Durham classification reservoir rock thin section images and their individual components (fossils and pore types) plus predict rock quality. The image recognition program's accuracy was tested on an unseen thin section image database.