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The Prudhoe Bay field, located on the North Slope of Alaska, is the largest oil and gas field in North America. The main Permo-Triassic reservoir is a thick deltaic high-quality sandstone deposit about 500 ft thick with porosities of 15 to 30% BV and permeabilities ranging from 50 to 3,000 md. The field contains 20 109 bbl of oil overlain by a 35 Tcf gas cap. The oil averages 27.6 API gravity and has an original solution gas-oil ratio (GOR) of about 735 scf/STB. Under much of the oil column area, there is a 20- to 60-ft-thick tar mat located above the oil-water contact (OWC).
Cyclic-gas-injection-based enhanced oil recovery (CGEOR) in the Eagle Ford was begun in late 2012 by EOG Resources and, at the time of writing, has expanded to more than 30 leases by six operators (266 wells). An extensive EOR evaluation was initiated to analyze the results recorded in these leases. The authors write that CGEOR in Eagle Ford volatile oil can yield substantial increases in estimated ultimate recovery (EUR) with robust economics, depending on compressor use and field life. The Eagle Ford shale represents some of the world's richest source rocks. The Upper Cretaceous seafloor received abundant organic debris and preserved it in an anoxic environment.
Abstract Openhole oil sampling in the tight Middle Cretaceous reservoirs of Alaska can be challenging due to the proximity of the reservoir pressure to the fluid’s saturation pressure. Existing focused probe technologies commonly used in other conditions have limited application in these conditions because their small flow area means slow pumping rates, high drawdowns, and nonrepresentative fluid samples. Nonfocused inlets, such as 3D radial probes and straddle packers, are mostly used to sample in these reservoirs, but deep invasion and slow pumping rates mean using these alternatives leads to long station times. A new wireline formation testing platform has been field tested in three wells since 2018. The objectives included the evaluation of the platform’s abilities to pump at controlled speeds to keep flowing pressures always above the fluid’s expected saturation pressures. A new inlet was tested for focused sampling and higher flow rates with the intention of cutting operating time and improving sample quality. Also, increased sample container capacity enabled the collection of required sample volumes in fewer bottles, which resulted in a shorter and lighter sampling string configuration. A legacy pressure tool was added to the bottom of the new platform for pressure testing benchmarking. During the operation, the tool was positioned at target depth, and an automated routine inflated the inlet assembly to contact the formation. This automation cycle enables the tool to be ready for pumping in less than 15 minutes. In contrast, technologies used in previous operations required 30 to 45 minutes setup time before fluid cleanup could commence. Fluids were then flowed through the tool’s sample and guard lines with a sequence of commingling and focused pumping periods using two simultaneous pumps while assessing fluid quality with a downhole fluid analyzer. Strict control of the 1-cm/s selected rate for both pumps provided fast cleanup in focused mode with less than 100-psi drawdown. This has never been achieved before in these reservoirs. First hydrocarbon breakthrough was observed less than an hour into the pumping period. Previous operations reported 4 hours or more for first hydrocarbon breakthrough. Three stations were performed, and 10 single-phase samples were collected in as many bottles. Thin-bedded interval testing was possible given the ability of the new platform to collect samples with either the sample or guard lines. Total operating time to complete the program was 30 hours. Comparison with data from similar operations in previous campaigns shows a decrease of 50% in operating time, faster rig- up and rig-down, and decreased cable tension. These latter two aspects add to operational efficiency and mitigation of risks. This case study summarizes several pioneering aspects of the new generation of wireline formation testing platforms. It was the first time a combination of the new and legacy technology was deployed and the first time that high-volume multiphase sample bottles were used during a field test. It was also one of the first applications of this new technology in North America.
Abstract It is well known that the NMR relaxation time T2 is proportional to the molecular mobility of water or hydrocarbons in rocks. In unconventional tight rocks, water and hydrocarbons are trapped in small pores of nanometer sizes, and their molecular mobility is severely restricted, causing the NMR T2 to be much shorter than that of conventional cases where pore sizes are in micrometer ranges. There are demands for advanced NMR techniques to study those solid-like bound hydrocarbons. In the meantime, it is of great interest for petrophysicists and geochemists to understand kerogen models in order to determine thermal maturity and hydrocarbon potential of organic-rich source rocks, and always attractive to have practical techniques that are nondestructive and less time consuming. In this study, a series of NMR 1D and 2D experiments have been performed on various types of source rocks with emphasis on short NMR T2 components, from sub-milliseconds down to a few microseconds, which are associated with kerogen, heavy hydrocarbons, and small hydrocarbon molecules bound in nanopores. The results show that the NMR CPMG pulse sequence used for the T2 data acquisition is (1) not capable of detecting and measuring the very rigid solid component of the T2 shorter than 30 microseconds, which is thought from kerogen, and (2) uncertain for the NMR components with T2 between 30 microseconds and 0.1 ms, which is dependent on the inter-echo spacing time (TE). Instead, the solid echo-pulse sequence was used to acquire the early time NMR signals that represent rigid solid matters, such as kerogen, in rock samples that have short relaxation times of less than 20 microseconds. The NMR solid echo signals were fitted into a composition of a Gaussian plus exponential functions to better describe NMR responses of source rocks with the shortest relaxation time of a few microseconds. The Gaussian component in the NMR signal is the measure of rigid solids associated with kerogen in the source rock. Model rock samples of thermally immature outcrops of the Upper Jurassic Kimmeridge Clay Formation in the UK and the Green River Shale Formation in the USA were used for comparison studies between the low field solid NMR techniques and geochemical analytical methods. The thermal maturities of the samples were artificially altered through the hydrous pyrolysis method at selected temperatures. The comparison results show that the amplitude of the Gaussian component measurement by NMR strongly correlated with the S2 of pyrolysis. The NMR relaxation times of the solid portion are directly proportional to the thermal maturity determined by organic petrography. This study concludes that the nondestructive solid NMR method provides an alternative and rapid way to study solid organic matters. The combined techniques enable us to further study kerogen models and hydrocarbon-generating potentials in organic-rich source rocks.
Abstract The necessity of knowing formation pressure is crucial to classifying pressure regimes for better understanding in well planning and to de-risk potential abnormal pressure conditions before any future field development wells are drilled, consequently minimizing operational cost. Wireline formation pressure testing has been a useful and reliable technology, that has evolved to confront the challenge of ultra-low permeable reservoir conditions by innovating and improving pump capability, accuracy in pressure measurements, automated control and the implantation of Formation Rate Analysis (FRA) intertwined with an Artificial Intelligent tool. In any pressure testing, the key factor is to be able to withdraw volume from the formation to generate a disturbance on formation pore pressure that a pressure gauge can measure. In the past this has been a difficult task in ultra-low permeable zones. The new generation of wireline tools are capable of withdrawing volume from ultra-low permeable reservoirs, with mobilities lower than 0.01mD/cP. This has been made possible by utilizing control of the pump speed down to 0.0003cc/s which then gives the operator the ability to test ultra-tight formations without the need for inflatable packers. By pulling down the pressure at an extremely low rate and using Artificial Intelligence to control the rate by knowing the formation rate, a proportional amount of volume can be extracted without calling it a tight test. During the operation by observing the rate, and making sure the pump is not oscillating, which indicates the formation rate is lower than the lowest rate the pump can withdraw, the test can be validated for formation flow and the pressure transient of the build – up can be analysed to confirm that at least spherical flow is observed. Once reservoir communication has been confirmed and by analysing drawdown and build-up pressure versus volume withdrawn and implementing the FRA equation, the reservoir pressure can be back calculated by considering isothermal compressibility and FRA slope. This paper highlights the best technical approach to quality check and quality control these tests, showing examples of various wells, where the technique has been used to predict a formation pressure, which can be used for further use for field development, drilling optimisation and production profiles. These pressures would never have been possible using static rates and volume.
Craddock, Paul (Schlumberger-Doll Research Center) | Srivastava, Prakhar (Schlumberger-Doll Research Center) | Datir, Harish (Schlumberger) | Rose, David (Schlumberger) | Zhou, Tong (Schlumberger) | Mosse, Laurent (Schlumberger) | Venkataramanan, Lalitha (Schlumberger)
Abstract This paper describes an innovative machine learning application, based on variational autoencoder frameworks, to quantify the concentrations and associated uncertainties of common minerals in sedimentary formations using the measurement of atomic element concentrations from geochemical spectroscopy logs as inputs. The algorithm comprises an input(s), encoder, decoder, output(s), and a novel cost function to optimize the model coefficients during training. The input to the algorithm is a set of dry-weight concentrations of atomic elements with their associated uncertainty. The first output is a set of dry-weight fractions of fourteen minerals, and the second output is a set of reconstructed dry-weight concentrations of the original elements. Both sets of outputs include estimates of uncertainty on their predictions. The encoder and decoder are multilayer feed-forward artificial neural networks (ANN), with their coefficients (weights) optimized during calibration (training). The cost function simultaneously minimizes error (the accuracy metric) and variance (the precision or robustness metric) on the mineral and reconstructed elemental outputs. Training of the weights is done using a set of several-thousand core samples with independent, high-fidelity elemental and mineral (quartz, potassium-feldspar, plagioclase-feldspar, illite, smectite, kaolinite, chlorite, mica, calcite, dolomite, ankerite, siderite, pyrite, and anhydrite) data. The algorithm provides notable advantages over existing methods to estimate formation lithology or mineralogy relying on simple linear, empirical, or nearest-neighbor functions. The ANN numerically capture the multi-dimensional and nonlinear geochemical relationship (mapping) between elements and minerals that is insufficiently described by prior methods. Training is iterative via backpropagation and samples from Gaussian distributions on each of the elemental inputs, rather than single values, for every sample at each iteration (epoch). These Gaussian distributions are chosen to specifically represent the unique statistical uncertainty of the dry-weight elements in the logging measurements. Sampling from Gaussian distributions during training reduces the potential for overfitting, provides robustness for log interpretations, and further enables a calibrated estimate of uncertainty on the mineral and reconstructed elemental outputs, all of which are lacking in prior methods. The framework of the algorithm is purposefully generalizable that it can be adapted across geochemical spectroscopy tools. The algorithm reasonably approximates a ‘global-average’ model that requires neither different calibrations nor expert parameterization or intervention for interpreting common oilfield sedimentary formations, although the framework is again purposefully generalizable so it can be optimized for local environments where desirable. The paper showcases field application of the method for estimating mineral type and abundance in oilfield formations from wellbore logging measurements.
Summary Ultra‐high‐pressure high‐temperature (uHPHT) reservoirs undergo extreme pressure depletion during their production life cycle. This results in significant reservoir compaction and consequent overburden subsidence with major consequences for wellbore mechanical integrity, safety, and field economics. However, the use of underdetermined geomechanical models to accurately predict compaction‐induced stress/strain changes on wellbores and its consequences during production time results in significant residual uncertainty. One method of measuring compaction‐induced stress/strain changes in wellbore is by the emplacement and measurement of radioactive markers. Although it is long established in normal pressure reservoirs, it is rare in uHPHT projects. The Culzean uHPHT gas‐condensate field is located in the UK Central North Sea. To constrain geomechanical model compaction uncertainty, radioactive markers were deployed. The objective was to accurately acquire preproduction baseline measurements and subsequent changes through periodic measurements during production life. These accurate wellbore measurements would then be compared with the geomechanical model to help calibrate predicted to actual compaction. By doing so, the objective is to enable better informed decisions regarding well and field management. The Culzean uHPHT radioactive marker project comprised a planning phase and a preproduction safe deployment including a baseline survey phase. Subsequent repeat measurements are planned during field production life. The emplacement and surveying of the subsurface radioactive markers for compaction monitoring in uHPHT reservoirs is a high value but nontrivial operation. In addition, much knowledge and experience of the methodology has been lost. This paper contributes to published literature by describing the successful emplacement and monitoring of subsurface radioactive markers on Culzean and aims to capture learnings and knowledge for future workers. Early detailed planning coupled with extensive testing is key to successful deployment. Timely engagement of all stakeholders and ensuring all decisions are aligned with safety and environmental considerations also contribute to realization of the project aims.
Summary The nonparametric transformation is a data-driven technique, which can be used to estimate optimal correlations between a dependent variable (response) and a set of independent parameters (predictors). This study introduces a systematic methodology using the nonparametric transformation concept and the alternating conditional expectation (ACE) algorithm to estimate the effective gas permeability using conventional logs and the core data. The ACE algorithm was employed in the current work using the MATLAB® (The MathWorks, Inc., Natick, Massachusetts, USA) code and the open-source GRaphical ACE (GRACE) software (Xue et al. 1997) for deriving the optimal nonparametric correlations for predicting the permeability. The methodology was applied to a heterogeneous formation [Bahariya (BAH)] in Egypt to understand its characteristics and predict its permeability more accurately. The BAH Formation is considered one of the main sources for oil production throughout the Western Desert (WD) of Egypt. The cumulative oil production from the BAH Formation is estimated to be approximately 40% of the total WD production. The reservoir characteristics of the BAH Formation range from highly permeable to tight sandstone interbedded with shale and siltstone. It usually depicts low-resistivity and low-contrast (LRLC) log behavior. Thus, regional and accurate determination of the reservoir permeability for the different rock units of the BAH Formation across the WD is a challenge. Conventional well log data from approximately 100 cored wells and corresponding 5,500 core measurements were used to provide a regional permeability correlation that can be used in a large number of reservoirs. The methodology of this work included two main steps: Applying the nonparametric transformation technique to identify the collective log responses for deriving optimal correlation Predicting the permeability profiles using the selected log responses The model was applied to many wells that address different petrophysical characteristics of the BAH Formation. The established permeability profiles showed reliable correlation coefficients relative to the measured core data. The correlation coefficient was 0.893 for the training data points (75% of the collected database) and 0.913 for the testing data points (25% of the collected database). In addition, the mean absolute percentage error (MAPE) between the predicted and the measured permeability for the training and testing data points were 5.93 and 4.14%, respectively. Permeability prediction using ACE is compared with other techniques such as k-ϕ crossplots, multiple linear regression (MLR), Coates, and Wyllie-Rosecorrelations. This work is considered an original contribution to present regional permeability prediction correlations using the conventional well logs for reservoir characterization and simulation applications. The ACE algorithm was successfully applied to the BAH Formation and proved its capability to identify the best predictors that are required to establish a rigorous model.
Summary The effects of temperature on the permeability coefficients of carbonaceous shales and the underlying mechanisms have been investigated experimentally. Pressure-pulse-decay gas-permeability tests were performed on seven shale plugs with different lithological compositions, organic-matter contents ranging from 0.8 to 11.7% total organic carbon (TOC) and thermal maturity between 0.53 and 1.45% random vitrinite reflectance (VRr). During the tests, the measuring temperatures were changed stepwise from 30 to 120°C and back to 30°C while axial load and confining pressure were kept constant. Sister plugs were used for mechanical tests to investigate the creep response upon thermal loading under the same temperature conditions. The samples showed varying degrees of permeability reduction by up to 71% with increasing temperature. This reduced permeability persisted during the cooling phase. The observed permeability changes reflect the elastoplastic deformation upon the thermal compaction of the rocks. Permeability reduction and creep response with increasing temperature are evidently controlled by organic matter, although clay minerals also played an important role. Organic-matter- and clay-rich shales exhibit the strongest response to temperature, while temperature effects were slightly smaller for overmature samples. Rock mechanical analysis showed that permeability reduction correlates with temperature-related creep/deformation of the shales. Given the strong temperature dependence of the mechanical stiffness of solid organic matter and of the viscosity of bituminous solids/liquids, more attention should be paid to temperature effects in the assessment of shale permeability. Our experimental results document that thermal stimulation has negative effects on shale-transport properties and that measurements conducted at laboratory temperatures can lead to substantial overestimation of in-situ shale permeability.
The Rumaila Field is in southeast Iraq and contains multiple reservoir intervals, including the Upper Cretaceous Mishrif carbonate reservoir, one of the major reservoirs in the world, that has been producing for more than 50 years. One of the key challenges in the Mishrif is to characterize the pore-structure distinction between primary and secondary porosity. The secondary porosity in the form of large pores, if present, dominates the petrophysical properties, especially permeability. Advanced logs, e.g., nuclear magnetic resonance (NMR) and image logs, can be used to understand the variations in pore structure, both qualitatively and quantitatively. In this paper, we focused primarily on four new wells with very comprehensive logging and coring programs. NMR logs were acquired using different tools and pulse sequences. This resulted in uncertainty in porosity and T2 distributions and, consequently, complications in the NMR interpretation. We observed two key issues: porosity deficit due to lack of polarization and T2 distribution truncation due to the low number of echoes. We used a single pore model to reproduce the NMR response in different pore sizes and fluid types for different pulse sequences. The results showed that the NMR response, especially in water-filled (water-based-mud filtrate) large pores, is sensitive to polarization time, echo spacing, and tool gradient strength. NMR log data confirmed the modeling results. We recommended an optimum pulse sequence and tool characteristics to fully capture the heterogeneous rock and fluid system in this carbonate reservoir. NMR logs, when available, were the primary tools to identify the large pores. We present a consistent workflow for NMR log analysis that was developed to identify and quantify large pores and extended to all wells in the field. We used advanced NMR interpretation techniques, e.g., factor analysis (NMR FA) (Jain et al., 2013), in a series of oil wells drilled with water-based mud. Using factor analysis, we identified a cutoff value of 847 ms for large pore volumes. In this manuscript, we also present an integration of laboratory measurements, e.g., NMR, mercury intrusion capillary pressure (MICP) data, whole-core CT scanning, and thin-section analysis, in our interpretation workflow. We also compared the large pore volume from image logs with NMR logs and other laboratory data and observed very consistent results. All the available information was integrated to build an “NMR-based” petrophysical model for porosity, rock type, permeability, and saturation determination. The NMR-based model was very comparable with the classic flow zone indicator (FZI) rock typing. The results of this study were used to modify the NMR acquisition program in the field and to build a petrophysical model based on only NMR and image log measurements for carbonate reservoirs. In this paper, we will discuss NMR modeling and corresponding log data from various wells to confirm the results. Furthermore, we will present a novel interpretation workflow integrating laboratory measurements and log data, which led to the modification of the NMR acquisition program in the field and the creation of a data-driven petrophysical model based on only NMR and image log measurements for carbonate reservoirs.