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Unmanned minimum facility platforms are a reliable alternative to traditional wellhead platforms or subsea installations, and the technologies enabling simpler designs have evolved. The technologies born out of innovative ideas have been critical for advancing deepwater assets in the past, and venture-capital investment helps incubate risk-taking companies developing those technologies. With digitization becoming a greater focus in industry, what role will venture capital play?
The decision may alleviate some of the pressures oil and gas producers faced in the wake of their imposition last year. Canada and Mexico made up a combined 20% of US imports of oil country tubular goods in 2017. An EIA report shows natural gas exports reaching 4.6 Bcf/D in February, the 13th consecutive month in which the country's natural gas exports exceeded its imports. Exports are projected to reach 7.5 Bcf/D by 2020.
The initial high cost of exploitation of the sustained, increasingly growing development of unconventional resources in Argentina has resulted in concentrating all efforts to increase well productivity while reducing construction and completion costs. The optimization of hydraulic fracture (HF) treatments is vitally important. It is the primary strategy used to achieve an optimal reservoir drainage area, consequently characterizing the fracture geometry, including the height, for the continuous improvement of HF treatment and planning.
Several types of technologies and methodologies are used to estimate fracture height during and after a hydraulic stimulation treatment. These technologies can provide information about the fracture geometry and extension in the near-wellbore (NWB) and far-field areas. The determination of a reliable correlation between those methodologies represents a challenge as a result of formation complexity, heterogeneity, and limitations of evaluation technologies. It is well-known that some areas in the Vaca Muerta formation contain layers that can act as fracture barriers and are responsible for fracture containment.
This paper presents a fast and simple methodology that uses conventional well logs [gamma ray (GR), sonic, and density] from pilot wells to identify potential fracture barriers. This approach establishes a means to evaluate the degree to which the rock will have the ability to control fracture height growth. This methodology was determined useful for planning perforation intervals or clusters placement, particularly in those formations with stress profile showing reduced stress contrast and, when complemented with geological information, this method also provides useful information for horizontal well trajectory. Case studies are provided to illustrate examples of the proposed fracture barrier index (FBI) being calibrated or compared to other fracture height assessment. Additionally, the benefits of adding this new approach to current methodologies and technologies to aid completion design optimization and decision making is discussed.
Relative permeability (kr) functions are among the essential data required for the simulation of multiphase flow in hydrocarbon reservoirs. These functions can be measured in the laboratory using different techniques including the steady state displacement technique. However, relative permeability measurement of shale rocks is extremely difficult mainly because of the low/ultralow matrix permeability and porosity, dominant capillary pressure and stress-dependent permeability of these formations.
In this study, the impacts of stress and capillary end effects (CEE) on the measured relative permeability data were investigated. The steady state relative permeability (SS-kr) measurements were performed on Eagle Ford and Pierre shale samples. To overcome the difficulties regarding the kr measurements of shale rocks, a special setup equipped with a high-pressure visual separator (with an accuracy of 0.07 cc) was used. The kr data were measured at different total injection rates and liquid gas ratios (LGR). In addition, to evaluate the impacts of effective stress, the kr data of an Eagle Ford shale sample were measured at two different effective stresses of 1000 and 3000 psi.
From the experimental data, it was observed that the measured SS-kr data of the shale samples have been influenced by the capillary end effects as the data showed significant variation when measured at different injection rates (with the same LGR). This suggested that the liquid hold-up (i.e. capillary end effects) depends on the competition of capillary and viscous forces. In addition, it was shown that it is more necessary to correct the experimental kr data measured at the lower LGRs. Furthermore, different relative permeability curves were obtained when the kr data were measured at different effective stresses. This behavior was explained as the capillary pressure was expected to be more dominant at the higher effective stress.
The results from this study improve our understanding of unconventional mechanisms in shale reservoirs. It is evident that the behavior of unconventional reservoirs can be better predicted when more reliable and accurate relative permeability data are available. The outcomes of this study will be useful for accurate determination of such kr data.
Yang, Tao (Equinor ASA) | Arief, Ibnu Hafidz (Equinor ASA) | Niemann, Martin (Equinor ASA) | Houbiers, Marianne (Equinor ASA) | Meisingset, Knut Kristian (Equinor ASA) | Martins, Andre (Teradata) | Froelich, Laura (Teradata)
Mud gas data from drilling operations provide the very first indication of the presence of hydrocarbons in the reservoir. It has been a dream for decades in the oil industry to predict reservoir gas and oil properties from mud gas data, because it would provide knowledge of the reservoir fluid properties in an early stage, continuously for all reservoir zones, and at low costs. Previous efforts reported in the literature did not lead to a reliable method for quantitative prediction of the reservoir fluid properties from mud gas data. In this paper, we propose a novel approach based on machine learning which enables us to predict gas oil ratio (GOR) from advanced mud gas (AMG) data.
The current work is based on a previous successful pilot in unconventional (shale) reservoirs. Our aim is to extend the results of the pilot study to conventional reservoirs. In general, prediction of reservoir fluid properties is more challenging for conventional reservoirs than for unconventional reservoirs, due to the complexity of petroleum systems in conventional reservoirs. Instead of building a model directly from AMG data, we trained a machine learning model using a well-established reservoir fluid database with more than 2000 PVT samples. After thorough investigation of compositional similarity between PVT samples and AMG data, we applied the model developed from PVT samples to AMG data.
The predicted GORs from AMG data were compared with GOR measurements from corresponding PVT samples to assess the accuracy of the GOR predictions. The results from 22 wells with both AMG data and corresponding PVT samples show large agreement between prediction vs. measurement. The accuracy of the predictive model is much higher than previous results reported in the literature. In addition, a Quality Check (QC) metric was developed to efficiently flag low-quality AMG data. The QC metric is vital to give confidence level for GOR prediction based on AMG data when PVT samples are not available.
The study confirms that AMG data can be used as a new data source to quantitatively predict continuous reservoir fluid properties in the drilling phase. The method can be used to optimize wireline operations and for some cases, it provides a unique opportunity to acquire reservoir fluid data when conventional fluid sampling or use of wireline tools is not possible. After high-quality PVT data becomes available in the wireline logging phase, the continuous GOR prediction can be further improved and used to determine reservoir fluid gradient and reservoir compartmentalization.
Hwang, Jongsoo (The University of Texas at Austin) | Sharma, Mukul (The University of Texas at Austin) | Amaning, Kwarteng (Tullow Ghana Limited) | Singh, Arvinder (Tullow Ghana Limited) | Sathyamoorthy, Sekhar (Tullow Ghana Limited)
Understanding injectivity is a critical element to ensure that sufficient volumes of water are being injected into the reservoir to maintain reservoir pressure, to ensure good reservoir sweep and minimize well remediation. It is, however, challenging to describe the large injectivity changes that are sometimes observed in injectors operating under fracturing conditions. This study presents a field case study with the following objectives: 1) explain the complicated injectivity changes caused by fracture opening/closure with injection-rate variations, 2) define a safe operating envelope (for injection pressure and rate) that ensures fracture containment and injection into the target zone, and 3) prescribe how the injection rate should be changed to achieve higher injectivities. Injector operating conditions are developed using results from a full 3-dimensional fracture growth simulation to ensure fracture containment in a multi-layered reservoir.
We present field injectivity observations, a comprehensive simulation workflow and its results to explain injector performance in a deep-water turbidite sand reservoir with multiple splay sands. Understanding the impact on fracture propagation and containment allows us to make quantitative suggestions for the operating envelopes for long-term injection-production management. Strategies for high-rate injection to sustain the injection well performance long-term are discussed.
Simulation results show that, at injection rates over 5,000 bwpd, injection induced fractures propagate. Fracture closure induced by injection shut-down is used to compute the bottom-hole pressure decline as a function of time. The fracture opening/closure events and the thermally induced stress were the primary factors impacting injectivity. The simulation results suggested several ways to improve the injectivity while ensuring fracture containment. Injection under fracturing conditions into a single zone at a high rate is shown to be feasible and this allows us to support a substantial increase in injectivity. This must, however, be done at pressures that will not cause a breach in the bounding shales. The 3-dimensional fracture simulations identified the operating pressure and rate envelope to maximize the injection rates while minimizing the risk of breaching the cap rock and inter-zone shales.
As an enhanced oil recovery method (EOR), chemical flooding has been implemented intensively for some years. Low Salinity WaterFlooding (LSWF) is a method that has become increasingly attractive. The prediction of reservoir behaviour can be made through numerical simulations and greatly helps with field management decisions. Simulations can be costly to run however and also incur numerical errors. Historically, analytical solutions were developed for the flow equations for waterflooding conditions, particularly for non-communicating strata. These have not yet been extended to chemical flooding which we do here, particularly for LSWF. Dispersion effects within layers also affect these solutions and we include these in this work.
Using fractional flow theory, we derive a mathematical solution to the flow equations for a set of layers to predict fluid flow and solute transport. Analytical solutions tell us the location of the lead (formation) waterfront in each layer. Previously, we developed a correction to this to include the effects of numerical and physical dispersion, based on one dimensional models. We used a similar correction to predict the location of the second waterfront in each layer which is induced by the chemical's effect on mobility. In this work we show that in multiple non-communicating layers, material balance can be used to deduce the inter-layer relationships of the various fronts that form. This is based on similar analysis developed for waterflooding although the calculations are more complex because of the development of multiple fronts.
The result is a predictive tool that we compare to numerical simulations and the precision is very good. Layers with contrasting petrophysical properties and wettability are considered. We also investigate the relationship between the fractional flow, effective salinity range, salinity dispersion and salinity retardation.
This work allows us to predict fluids and solute behaviour in reservoirs with non-communicating strata without running a simulator. The recovery factor and vertical sweeping efficiency are also very predictable. This helps us to upscale LSWF by deriving pseudo relative permeability based on our extension of fractional flow and solute transport into such 2D systems.
Oil price forecasting has been shown to be challenging if not impossible for the long-term. However, the oil price has a major impact on Exploration and Production projects.
Historical Project Realized Oil Price (PROP) can be calculated for example projects by summing up the total project revenue using the actual oil prices and dividing through the total amount of oil produced. For different starting dates of example projects, the PROP changes. Determining the PROP for different starting times, a Cumulative Distribution Function (CDF) can be derived. Adjusting this CDF for expected "half cycle breakeven costs" for the low limit and demand considerations for the high case leads to a PROP range that can be used for future project evaluation.
Including PROP ranges into project evaluation allows for the selection of the most attractive development option, Value of Information analysis and project Probability of Economic Success (PES) calculation including oil price uncertainty.
Furthermore, using PROP ranges rather than oil price scenarios enables a distinction between short-term budget planning and long-term project development. For budget planning, a scenario approach is suggested while for long-term planning PROP ranges should be used. Applying long-term planning on PROP ranges leads to less fluctuation in staff planning and small annual adjustments in PROP range forecasting. Also, using PROP ranges results in increasing PES project hurdles at low oil prices and lower PES hurdles at high oil prices. Hence, at low oil prices the risk averseness of the company is increased. Another effect of using PROP ranges is that at high oil prices robustness of projects to low oil prices is included in the assessment.
To investigate the effect of PROP ranges on portfolio PES hurdles and project PES hurdles, a simplified linear-fit-model was developed. The results of the model showed that the project PES hurdles in a Value at Risk assessment can be determined applying the linear-fit-model to quantify the oil price dependency. The required individual project PES hurdles can be adjusted using the linear-fit-model to account for oil price uncertainty.