Source rock reservoirs have complex mineralogies and pore systems—their porosity varies from low to moderate, and their permeability varies from low to ultra-low and is in the nano-darcy range. The petrophysical evaluation of these complex reservoirs using only conventional logging tool (quad combo) data is highly complex and uncertain as a result of the tools’ limitation. Obtaining better and more certain petrophysical results for these reservoirs require integrating data from the conventional logging tool with the high-tech tools [elemental spectroscopy, nuclear magnetic resonance (NMR), dielectric, and spectral gamma ray].
It is challenging to calculate accurate porosity and water saturation values of source rock reservoirs using conventional log data. The presence of kerogen and complex mineralogies, such as conductive minerals, increases the difficulty. Because these petrophysical outputs are important for reserve estimates, any calculation error of these components directly affects the hydrocarbon in-place estimates. To address this issue, a new workflow has been developed to integrate data from conventional logging and high-tech tools.
This paper demonstrates a workflow designed to characterize source rock reservoirs by integrating data from conventional logging and high-tech tools. Additionally, this workflow helps provide accurate petrophysical outputs that are used later to estimate the correct hydrocarbon in place. This workflow was tested in multiple source rock reservoirs in the Middle East region. This workflow also provides mechanical properties (including Poisson's ratio, Young's modulus, closure pressure, and brittleness) of source rock reservoirs. The integration of rock mechanics and petrophysical results helps identify sweet spots that can be selected for fracturing operations.
Unconventional reservoirs have low porosity and permeability, making it difficult to produce from them. Generally, enhanced reservoir techniques, such as fracture stimulation or steam injection, are necessary to produce commercially from these reservoirs. There are number of unconventional reservoirs globally, such as tight gas sands, coal bed methane, tar sands, gas hydrates, shale gas, and shale oil.
This paper presents a new methodology for performing a cutoff analysis that uses a T1/T2 ratio distribution obtained from two-dimensional (2D) nuclear magnetic resonance (NMR) T1 and T2 measurements. The ability to classify pores and their effect on permeability is noticeably improved compared to a T2-based approach, of which T2 cutoff values vary from a few tens of milliseconds to a few seconds. Based on mercury injection capillary pressure results, the T1/T2 ratio-based cutoff is used to differentiate porosity with a pore throat radius larger than 2 µm from smaller pore throats. The T1/T2 cutoff ranges narrowed to within 1.4 to 1.7 for 100% water-saturated carbonate cores. In addition, the empirical models of NMR-based permeability are enhanced by incorporating the porosity, T1/T2 ratio cutoff, and T2 geometric mean. For the studied data set of 49 carbonate rock samples with a permeability range spanning six orders of magnitude, an excellent correlation coefficient of R2 = 0.9 was observed between the NMR predicted permeability and that measured in the laboratory. This improved permeability prediction technique has the potential to be implemented in applications of downhole NMR logging.
Timely and detailed evaluation of in-situ hydrocarbon flow properties such as oil density and viscosity is critical for successful development of heavy oil reservoirs. The prediction of fluid properties requires comprehensive integration of advanced downhole measurements such as nuclear magnetic resonance (NMR) logging, formation pressure, and mobility measurements, as well as fluid sampling.
The reservoir rock presented in this paper is an unconsolidated Miocene formation comprising complex lithologies including clastics and carbonates. The reservoir fluids are hydrocarbons with significant spatial variations in viscosity ranging from (60-300 cP) to fully solid (tar). Well testing and downhole fluid sampling in this formation are hindered by low oil mobility, unconsolidated formation that generates sand production, emulsion generation, and very low formation pressure.
We present a two-pronged log evaluation workflow to identify sweet spots and to predict fluid properties within the zones of interest. First, the presence of "missing NMR porosity" and "excess bound fluid" is estimated by comparing the NMR total and bound fluid porosity with the conventional total porosity and uninvaded water-filled porosity logs, respectively. Secondly, two-dimensional NMR diffusivity vs. T2 NMR analysis is performed in prospective zones where lighter and, possibly, producible hydrocarbons are detected. The separation of oil and water signals provides a resistivity-independent estimation of the shallow water saturation. Additionally, we correlated the position of the NMR oil signal with oil-sample viscosity values. The readily available log-based viscosity greatly improves the efficiency of the formation and well-testing job.
We successfully sampled high viscosity hydrocarbon fluids by utilizing either oval pad or straddle packer. The customized tool designed for sampling aided gravitational segregation of clean hydrocarbons from the water-based mud filtrate and emulsion; and therefore providing representative reservoir fluid samples based on downhole fluid analyzers.
A refined radial-basis-function (RBF) method with a forward selection algorithm to improve the stability of the prediction of pore throat sizes was recently reported by the authors. Subsequently, from the pore throat size distribution data, permeability and pore typing models were developed. These models were developed with the core samples from the high-to-medium quality reservoir sections of several Middle East carbonate wells.
Because the RBF is an interpolation method, the validity of RBF based petrophysical models is enveloped by the petrophysical parameter range that the core samples represent. For economic reasons, it is common practice that core analysis be conducted on high reservoir quality rock samples because they are most important to production. To apply RBF-based models for interpreting well logging data, it is important that such models be developed with a broad range of rock qualities to help prevent misinterpreting the lower-quality formation rocks.
To expand the application envelop of the RBF based nuclear magnetic resonance (NMR) permeability models, a new set of core measurements from different reservoir quality sections, as well as non-reservoir quality sections of several carbonate wells, are added to retrain the RBF-based NMR permeability models. Standard statistical validation methods are used to demonstrate the necessity and improvements of newly retrained RBF-based models. The new models are applied to well logging data with varying reservoir quality sections, proving that the new models are adequate for better permeability prediction of all rock quality formations.
It is well-known that organic shale reservoirs have very low permeability. Any stimulated fracture system is influenced by extensive horizontal laminations that are pervasive in shale reservoirs. The laminations will strongly influence the hydraulic fracture height because of the difference in measured and predicted rock mechanical properties normal and parallel to the bedding planes. To accurately predict fracturing height and even fracture width from logs in this environment, these mechanical property differences must be considered. This is accomplished by predicting from logs the Young’s modulus and Poisson’s ratio parallel to the bedding planes (referred to as the horizontal Young’s Modulus (Ehorz) And Poisson’s ratio (Vhorz), respectively) and also the Young’s modulus and Poisson’s ratio normal to the bedding planes (referred to as the vertical Young’s modulus (Evert) and Poisson’s ratio (Vvert), respectively). These predictions are typically derived by density/sonic measurements used in conjunction with the ANNIE equations (Schoenberg et al. 1996). Review of the publications using ANNIE model (Higgins et al. 2008; Waters et al. 2011) reveals that ANNIE always predicts vvert = vhorz. This is shown to be the general case for ANNIE.
The formation evaluation of Saudi Arabian reservoirs presents multiple challenges. The complexities encountered include varying mineralogy and mixed lithologies, a wide range of porosities and pore types, hydrocarbon viscosity, and variable formation water salinities.
Two-dimensional (2D) analysis of NMR data acquired with simultaneous T1-T2 has proven to be beneficial for the identification and quantification of hydrocarbon-bearing reservoirs and providing valuable information about porosity and reservoir quality.
NMR porosity measurements are free from mineralogical effects and, therefore, provide a very good estimate of formation porosity. Moveable and bound fractional fluid porosities from NMR provide additional reservoir information and are used for estimating permeability. Simultaneous T1-T2 acquisition and two dimensional analyses provide graphic 2D identification for the presence of hydrocarbons and hydrocarbon type, as well as a volumetric estimate of near wellbore hydrocarbons independent of formation water resistivity.
Results from a simultaneous NMR T1-T2 acquisition are compared to formation tester results. The strong correlation between the NMR predictions and the formation tester results suggests this method is effective in the evaluation of challenging formations and might also be applicable to other reservoirs.
Musharfi, Nedhal (Saudi Aramco) | Almarzooq, Anas (Saudi Aramco) | Eid, Mahmoud (Halliburton) | Quirein, John (Halliburton) | Witkowsky, Jim (Halliburton) | Buller, Dan (Halliburton) | Rourke, Marvin (Halliburton) | Truax, Jerome (Halliburton) | Praznik, Greg (Halliburton)