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Kumar, Ashish (The University of Texas at Austin) | Seth, Puneet (The University of Texas at Austin) | Shrivastava, Kaustubh (The University of Texas at Austin) | Manchanda, Ripudaman (The University of Texas at Austin) | Sharma, Mukul M. (University of Texas)
In ultralow-permeability reservoirs, communication between wells through connected fractures can be observed through tracer and pressure-interference tests. Understanding the connectivity between fractured horizontal wells in a multiwell pad is important for infill well drilling and parent-child well interactions. Interwell tracer and pressure-interference tests involve two or more fractured horizontal wells and provide information about hydraulic-fracture connectivity between the wells. In this work, we present an integrated approach based on the analysis of tracer and pressure interference data to obtain the degree of interference between fractured horizontal wells in a multiwell pad.
We analyze well interference using tracer (chemical tracer and radioactive proppant tracer) and pressure data in an 11-well pad in the Permian Basin. Changes in pressure and tracer concentration in the monitor wells were used to identify and evaluate interference between the source and monitor wells. Extremely low tracer recovery and weak pressure response signify the absence of connected fractures and suggest that interference through matrix alone is insignificant. Combined tracer and pressure-interference data suggest connected fracture pathways between the communicating wells. The degree of interference can be estimated in terms of pressure response times and tracer recovery. An effective reservoir model was used to simulate pressure interference between wells during production. Simulation results indicate that well interference observed during production is primarily because of hydraulically connected fractures.
Combined tracer and pressure-interference analysis provides a unique tool for understanding the time-dependent connectivity between communicating wells, which can be useful for optimizing infill well drilling, well spacing, and fracture sizing in future treatment designs.
Quantifying wettability of organic-rich mudrocks is important for reliable formation evaluation, optimizing production, predicting water/hydrocarbon production, and selection of appropriate fracture fluids. Recent publications suggest that kerogen wettability can vary as a function of thermal maturity, ranging from water- to hydrocarbon-wet at low to high thermal maturities, respectively. However, clay minerals tend to preferentially be water-wet. It is therefore important to determine which of these constituents have a dominant contribution to overall wettability of the rock. To answer this question, we introduce methods to quantify the relative water-adsorption capacities of clay minerals, kerogen, and organic-rich mudrocks at different thermal-maturity levels. We started with isolating kerogen from organic-rich mudrock samples using chemical and physical separation methods and synthetically matured them to different thermal-maturity levels. We then prepared synthetic organic-rich mudrock samples by mixing known quantities of clay minerals, nonclay inorganic minerals, and kerogen. We then performed water-vapor adsorption measurements on pure clay minerals, pure kerogen samples, and synthetic organic-rich mudrock samples under controlled humidity conditions. Nuclear magnetic resonance (NMR) measurements were then used to quantify the volume of water adsorbed on clay minerals and organic-rich mudrock samples. We used the flotation test to qualitatively assess the wettability of the synthetic organic-rich mudrocks.
Water-vapor adsorption experiments showed that the volume of water adsorbed on the surface of nonheated kerogen samples at low thermal maturities is 5.31 mL/100 g, which decreases significantly to 0.09 mL/100 g when the kerogen sample is heat-treated to 450°C. The results can be attributed to strong attraction between the oxygen content in kerogen and water at low thermal maturities. We quantified the water-adsorption capacity of kerogen samples heat-treated at 450°C and found that volume of water adsorbed decreases with an increase in thermal maturity both in the presence and absence of bitumen. In the case of synthetic organic-rich mudrock samples, we found that the volume of water adsorbed in samples at higher thermal maturity decreases by 16% compared with organic-rich mudrocks at low thermal maturity at the same concentration of nonswelling clay minerals. Results from the flotation test showed that the oil-wettability of the synthetic organic-rich mudrock samples increases as its thermal maturity decreases, with a hydrogen index (HI) of 328 to 54 mg hydrocarbon/g organic carbon (mg-HC/g-OC). Results confirmed that kerogen and its geochemistry can have a significant influence on the overall wettability of organic-rich mudrocks even at low concentrations of 4 wt%. The outcomes of this paper can contribute to a better understanding of the parameters affecting wettability of organic-rich mudrocks and are promising for in-situ assessment of their wettability. This can potentially contribute to improved understanding of flow mechanisms in organic-rich mudrocks, which control hydrocarbon/water production.
Reamers are an integral part of deepwater Gulf of Mexico (GOM) drilling and their performance significantly impacts the economics of well construction. This paper presents a novel programmatic approach to model rate of penetration (ROP) for reamers and improve drilling efficiency. Three field implementations demonstrate value added by the reamer drilling optimization (RDO) methodology.
Facilitated by user interface panels, the RDO workflow consists of surface and downhole drilling data filtering and visualization, detection of rock formation boundaries, frictional torque (FTRQ) and aggressiveness estimation, ROP modeling with analytical equations and machine learning (ML) algorithms [regression, random forests, support vector machines (SVMs), and neural networks], and optimization of drilling parameters. ROP model coefficients and bit and reamer aggressiveness are dependent on lithology and computed from offset well data. Subsequently, when planning a nearby well, bottomhole assembly (BHA) designs are evaluated on the basis of drilling performance and weight and torque distributions between cutting structures to avoid early reamer wear and dysfunctions. Geometric programming establishes optimal drilling parameter roadmaps according to operational limits, downhole tool ratings, rig equipment power constraints, and adequate hole cleaning.
Separate ROP models are trained for reamer-controlled and bit-controlled ROP zones, defined by the proportion of surface weight on bit (WOB) applied at the reamer, in every rock formation. This novel concept enables ROP prediction with the appropriate model for each well segment depending on which cutting structure limits drilling speed. In the first of the three RDO applications with field data from deepwater GOM wells, optimal bit-reamer distances are determined by analyzing reamer weight load in uniform salt sections. Next, ROP modeling for the addition or removal of a reamer from the BHA is used in contrasting well designs to conceivably alleviate a USD 16 million casing inventory surplus. Finally, active optimization constraints are investigated to reveal drilling performance limiters, justifying equipment upgrades for a future deepwater GOM well.
The proposed innovative workflow and methodology apply to any drilling optimization scenario. They benefit the practicing engineer interested in drilling performance optimization by providing insights on how different cutting structure sizes affect ROP behavior and ultimately aiding in the selection of appropriate bit and reamer diameters and optimal operational parameters.
Accurate and frequent mud checking is essential for optimum drilling operations. Careful measurement and maintenance of drilling fluid properties (density and rheology) maintain the primary well control barrier and optimize fluid hydraulics and hole-cleaning ability. However, a full mud report while drilling is provided only once or twice a day. Moreover, the measurements are mostly performed using traditional equipment. Test interpretation is subjective and might be biased and difficult to verify independently.
In this paper, we present an automated mud skid unit (MSU), which performs continual drilling fluid sampling and measurements at variable temperatures. The unit provides the non-Newtonian rheological constants characterizing a yield-power law fluid as well as the real-time friction factor and critical Reynolds number using a pipe-viscometer measurement approach. Other important fluid properties such as pressurized density, oil/water ratio, and temperature are provided using high-quality in-line sensors. The unit is controlled by a programmable logic controller coupled with a Linux operating system for data analysis. The system sends real-time data to WITSML data servers and provides detailed mud reports to engineers working either on-site or remotely.
The MSU was deployed in the Permian Basin by an independent operator for automated mud monitoring during unconventional shale drilling operations. Rheology, density, and phase content measurements were compared with conventional mud reports provided by the on-site mud engineer. High accuracy (error<5%) was observed in mud rheology tests. The pressurized mud-density measurements provided by the MSU proved to be more accurate than nonpressurized mud balance measurements, which were affected by mud aeration. Moreover, the MSU provided mud check data 25 times more frequent than those generated by the mud engineer at temperatures of 50 and 65.5°C. Drilling-fluid-related issues, such as chemical overtreatment as well as sudden changes in mud density, rheology, and oil/water ratio, were reported immediately to the drilling crew. This paper provides details about the measurement technology as well as the results from the field deployment of the MSU.
Zhao, Bochao (The University of Texas at Austin) | Ratnakar, Ram (Shell International Exploration & Production) | Dindoruk, Birol (Shell International Exploration & Production and University of Houston) | Mohanty, Kishore (The University of Texas at Austin)
Accurate estimation of relative permeability is one of the key parameters for decision making in upstream applications from project appraisal to field development and evaluation of various field development options. In this study, we identify Euler number (Arns et al. 2001) (a quantitative measure of fluid connectivity/distribution) and saturation as being the first-order predictors of relative permeability and develop a reliable correlation between them using machine learning of experimental special core analysis (SCAL) data and pore network simulation results.
In order to achieve our objective, first, we developed a machine-learning model based on the random forest algorithm (Breiman 2001) to analyze specific SCAL data that indicates a key missing feature in the traditional saturation-based relative permeability prediction. We identified this missing feature and proposed the Euler characteristic as a potential first-order predictor of relative permeability in combination with in-situ fluid saturations. We generated “artificial” relative permeability data using pore network simulation (Valvatne and Blunt 2004) by systematically varying a set of key parameters such as pore geometry, wettability, and saturation history. Subsequently, we used machine learning to rank the importance of each parameter and identify possible correlative responses to those selected variables.
At a fixed saturation (zero-dimensional volumetric abundance) and Euler number coordinates, the relative permeability is very consistent and varies insignificantly across different cases, suggesting these two parameters as first-order predictors. Euler number characterizes the fluid connectivity/distribution, while saturation represents the net volumetric fluid quantity. We believe that Euler number could be the missing first-order predictor in traditional saturation-based predictive relative permeability models, especially for connected pathway dominated flow regime. Finally, we identified the quantitative relationship between relative permeability and Euler characteristic, and present a reliable correlation to determine the relative permeability on the basis of Euler number and saturation.
Sun, Zhuang (The University of Texas at Austin) | Tang, Hewei (Texas A&M University) | Espinoza, D. Nicolas (The University of Texas at Austin) | Balhoff, Matthew T. (The University of Texas at Austin) | Killough, John E. (Texas A&M University)
Summary The reduction of pore pressure caused by depletion can induce significant reservoir compaction and loss of permeability, especially in unconsolidated reservoirs. In this paper, we develop a numerical approach on the basis of computer-based simulations of unconsolidated rock samples subjected to mechanical tests that replicate the one-dimensional (1D) strain depletion path and allow for a prediction of permeability loss. The 1D strain stress path is a good approximation for long and thin conventional reservoirs with a compliant caprock. The numerical sample consists of crushable stiff and soft grains (proxies for sand and shale) simulated with the discrete element method (DEM) coupled with the bonded-particle model (BPM). Model parameters are calibrated through numerical single-graincrushing tests which reproduce the experimentally measured sand strength. Grain crushing induced by the uniaxial strain stress path results in a pronounced reduction of porosity and permeability, which manifests more readily for samples with large grain size. The change of particle-size distribution indicates that high effective stresses cause grain crushing and production of a significant amount of fines that lower permeability. Simulation results indicate that the presence of soft grains and inclusions (e.g., shale fragments) facilitates grain crushing. Reservoir simulations--incorporating the change of porosity and permeability as a compaction table--show that the compaction can enhance cumulative production due to compaction drive but also reduces production rate by impairing the reservoir permeability. Introduction Depletion-induced reservoir compaction is an important issue during hydrocarbon production, especially for unconsolidated formations (Ostermeier 1995). For instance, most deepwater Gulf of Mexico (GOM) reservoirs that are highly overpressured can experience large pore pressure depletion over the productive life with limited aquifer support (Settari 2002). On the other hand, reservoir compaction can also provide an additional driving force for hydrocarbon production (Merle et al. 1976; Puig et al. 1984; Fisher et al. 1999; Walsh and Lake 2003). Prediction of the compaction-induced changes in porosity and permeability can help optimize drilling and completion strategies (Schutjens et al. 2004). Compaction is dependent on the initial stress state and the depletion stress path.
D'Angelo, John (The University of Texas at Austin) | Pehlivanturk, Can (The University of Texas at Austin) | Ashok, Pradeepkumar (The University of Texas at Austin) | van Oort, Eric (The University of Texas at Austin) | Shahri, Mojtaba (Apache Corporation) | Vasicek, Alan (Apache Corporation) | Behounek, Michael (Apache Corporation)
Accurately estimating the directional tendency of a drill string is important to ensure tracking of the planned trajectory and to prevent over/undershooting targets. Predicting this tendency is often achieved through calibration of a physical model of wellbore propagation using previous directional drilling data. However, the amount of relevant prior information to use and the uncertainty in future predictions is not always clear.
The goal of the work reported here was to determine the amount of prior directional drilling data to use for calibration, and the frequency at which recalibration should occur to ensure accurate predictions of wellbore propagation. A procedure was designed to provide this information for a given wellbore propagation model in a given field. The particular model used in this study is a quaternion-based wellbore propagation model that describes the motion of the bit as a rigid body in space which moves based on fixed angular velocities and drilling inputs given in the form of a traditional slide sheet. However, the procedure can be used for any wellbore propagation model that requires calibration on prior directional drilling data.
The procedure consists of two tests. The first test (hold-out test) shows how the prediction error varies over an increasing amount of calibration data while holding the testing-set size constant. The second test (prediction test) shows how the prediction error varies over an increasing testing-set size while holding the amount of calibration data constant. These variations in prediction error can then be examined by looking at the spread of data over these tests.
The test methodology was applied on field data obtained from North American wells where bent sub motors were used for deviation control. The first test showed diminishing returns when incorporating more actions into the model calibration. In cases where bottom-hole assembly (BHA) changes were observed, calibrating on a larger number of prior actions slightly increased the test error. The second test allowed us to approximate the error propagation rate of our model predictions in the current field. The procedure described in this paper allows for such information to be quantified and visualized easily to improve the directional driller's understanding of the needs and reliability of the wellbore propagation model being used.
One of the primary functions of the drilling fluid is to transport cuttings from the bit to the surface. This transport is mainly a function of fluid properties (rheology and density), pump rate, wellbore trajectory and geometry, drillstring rotation and eccentricity. Because these interrelated parameters all have a significant effect on cuttings transport, it is important that hole cleaning modeling is comprehensive and inclusive to yield useful, reliable results. Inaccurate models that either do not consider significant effects or oversimplify them can lead to inadequate hole cleaning advice, resulting in non-productive and invisible lost time.
A novel cuttings transport model that includes relevant effects such as pipe rotation, eccentricity and the annular blockage is presented. Local velocity profiles are calculated for a given fluid and compared against the local critical velocity for cuttings transport. Then, the location and the magnitude of annulus blockage are numerically assessed. After an annulus becomes partially blocked with cuttings deposited in a cuttings bed, continuity and momentum equations are solved for the blocked annulus to estimate the new local velocities. The annulus is divided into small sections and the pressure profile is calculated with the progression of time. By doing so, a realistic representation of a wellbore's pressure and velocity profiles is obtained.
The results from this study are in very good agreement with large-scale flow loop experiments and field observations, e.g. showing that axial flow alone is not enough for effective hole cleaning in high deviation and horizontal wells. The simulations show that in such wells the cuttings tend to deposit even at elevated axial flow rates without pipe rotation in a fully eccentric annulus, due to a significantly reduced flow rate where the drillstring is close to the borehole or previous casing string. The results also provided a way to predict "pack-off" events, when the string can become stuck, the equivalent circulating density can spike, and lost circulation can occur. The non-uniform settling of the cuttings along the wellbore will cause non-uniform distribution of pressure losses, with local pressure spikes when cuttings blockage becomes substantial. A pack-off sensitivity analysis is presented in this work. The overall aim of this work is to provide more comprehensive cuttings transport modeling for the industry to conduct safe and improved hole cleaning operations.
Poor hole cleaning leads to problems such as pack-offs, stuck pipe incidents, and lost circulation events due to increased equivalent circulating density (ECD). Hole cleaning issues can be mitigated by using a digital twinning system that constantly monitors borehole condition with real-time data and process models, and suggests optimal actions. The solution space of possible actions, however, is very large causing computational hurdles for real time implementation. This paper describes a time-efficient digital twinning approach that uses reinforcement learning (RL) to simulate scenarios corresponding to multiple hole cleaning actions.
Digital twinning of cuttings transport in a wellbore requires the development of a system with multiple integrated physics and data-based models to quantitatively detect symptoms of impending issues early, and to take actions to mitigate these problems. First, the state of the borehole is quantified in terms of ECD, concentration of cuttings in flow, and cuttings bed height. Next, a solution space of all legal and feasible actions based on the current system state is obtained. Finally, the alternate action sequences are evaluated to suggest the most suitable path forward.
To classify the state of the borehole in terms of ECD, cuttings bed height and cuttings concentration in the flow stream, hydraulics and cuttings transport models were implemented, tested and validated against real field and experimental data. Based on the state of the system, allowable actions were evaluated in terms of some combination of hole cleaning parameters that can be controlled in near real-time, such as flow rate, rotary speed, mud density, mud rheology and the weight on bit for rate of penetration (ROP) control. To limit the size of the solution space, those actions that were unrealistic for implementation in real-time were discarded. Also, any actions that violated the limits of the available drilling margin were discarded. Finally, by defining immediate rewards associated with different states-action pairs based on their effect on the wellbore stability and hole condition, the concepts of Markov reward process (MRP) and scenario realizations were used to suggest the optimal action for a given state.
The predicted output of the algorithm for multiple operational scenarios was validated by comparing it with actions that a hole cleaning / extended reach drilling (ERD) expert would have taken when given similar scenarios. The automated process of identifying hole cleaning system states, generating multiple viable hole cleaning action sequences, and finally evaluating the probability of successful hole cleaning for multiple actions in real-time and deciding the best course of action, is a novel contribution of this work. It will benefit practitioners who struggle with hole cleaning / stuck pipe-related non-productive time and pave the way for hole cleaning automation.
van Oort, Eric (The University of Texas at Austin) | Juenger, Maria (The University of Texas at Austin) | Aldin, Munir (Metarock Laboratories) | Thombare, Akshay (Metarock Laboratories) | McDonald, Mike (National Silicates, an affiliate of PQ Corporation)
Well abandonment is one of the biggest challenges in the oil and gas industry, both in terms of cost and effort as well as the technical hurdles associated with wellbore isolation for an indefinite term. A mechanism that may be exploited to simplify well abandonments is using natural shale formations for the creation of annular barriers. Currently, uncemented annuli often require casing milling and pulling before abandonment plugs can be set, which necessitates the use of a drilling rig. This is an expensive, time- and labor-intensive process, particularly offshore. However, shale creep may naturally form a barrier behind uncemented casing sections. With a qualified annular shale barrier in place, the well may only require the setting of abandonment plugs within the existing casing string(s), a task that can often be done rigless and with significantly less effort.
The work described in this paper presents the results of a rock mechanical investigation into the creep behavior of North Sea shales and their ability to form effective annular barriers. Field core from the Lark-Horda shale was used to conduct dedicated, customized experiments that simulated the behavior of shale confined under downhole effective stress, pressure and temperature conditions to fill in an annular space behind a simulated casing string. Full scale tri-axial rock mechanics equipment was used for testing cylindrical shale samples obtained from well-preserved field core in a set-up that mimicked an uncemented casing section of a well. The deformation behavior of the shale was monitored for days to weeks, and the formation of the annular barrier was characterized using dedicated strain measurements and pressure pulse decay probing of the annular space.
The large-scale lab results clearly show that the Lark-Horda shales will form competent low permeability annular barriers when left uncemented, as confirmed using pressure-pulse decay measurements. They also show that experimental conditions influence the rate of barrier formation: higher effective stress, higher temperature and beneficial manipulation of the annular fluid chemistry all have a significant effect. This then opens up the possibility of activating shale formations that do not naturally create barriers by themselves into forming them, e.g. by exposing them to low annular pressure, elevated temperature, different annular fluid chemistry, or a combination. The results are in very good agreement with field observations reported earlier by several North Sea operators.