Characterizing the fractures is an important task to improve the understanding and utilization of hydraulic fracturing. As an approach to augment and improve on the existing methods, time-lapse electric potential measurements could be used to characterize subsurface features. In this study we investigated the characterization of fracture length and fracture density by using time-lapse electric potential data. A new borehole ERT (electric resistivity tomography) method designed specifically for hydraulic fracture characterization is proposed to better capture reservoir dynamics during hydraulic fracturing. This method uses high resolution electric potential data by implementing electrodes in or near boreholes and monitor electric potential distribution near the horizontal fracture zone. The time-lapse electric potential data generated by this tool were simulated and subsequently used to analyze fracture characteristics. Inverse analysis was then performed on the electric potential data to estimate fracture length and fracture density. Last, we performed sensitivity analysis to examine the robustness of the estimates in nonideal environments. The results of this work show that time-lapse electric potential data are capable of capturing flow dynamics during the fracturing process. Using the proposed borehole ERT method we successfully estimated the true fracture length and true fracture density of a constructed fracture model. We were able to determine the best locations in the constructed reservoir to place the electrodes, and through sensitivity analysis we found the maximum noise level of the electric potential data that can still allow the proposed method to make robust fracture length and fracture density estimates.
Our proposed method offers a new approach to make robust estimates of fracture length and fracture density. Electric potential data have been used mostly for well logging in the past. This study demonstrates a novel way of using electric potential data in unconventional development and opens possibilities for more applications such as production monitoring.
Wheeler, Mary F. (The University of Texas at Austin, USA) | Srinivasan, Sanjay (Pennsylvania State University, USA) | Lee, Sanghyun (Florida State University, USA) | Singh, Manik (Pennsylvania State University, USA)
Optimal design of hydraulic fractures is controlled by the distribution of natural fractures in the reservoir. Due to sparse information, there is uncertainty associated with the prediction of the natural fracture system. Our objective here is to: i) Quantify uncertainty associated with prediction of natural fractures using micro-seismic data and a Bayesian model selection approach, and ii) Use fracture probability maps to implement a finite element phase-field approach for modeling interactions of propagating fractures with natural fractures.
The proposed approach employs state-of-the-art numerical modeling of natural and hydraulic fractures using a diffusive adaptive finite element phase-field approach. The diffusive phase field is defined using the probability map describing the uncertainty in the spatial distribution of natural fractures. That probability map is computed using a model selection procedure that utilizes a suite of prior models for the natural fracture network and a fast proxy to quickly evaluate the forward seismic response corresponding to slip events along fractures. Employing indicator functions, diffusive fracture networks are generated utilizing an accurate computational adaptive mesh scheme based on a posteriori error estimators.
The coupled algorithm was validated with existing benchmark problems which include prototype computations with fracture propagation and reservoir flows in a highly heterogeneous reservoir with natural fractures. Implementation of a algorithm for computing fracture probability map based on synthetic micro-seismic data mimicking a Fort Worth basin data set reveals consistency between the interpreted fracture sets and those observed in the reference. Convergence of iterative solvers and numerical efficiencies of the methods were tested against different examples including field-scale problems. Results reveal that the interpretation of uncertainty pertaining to the presence of fractures and utilizing that uncertainty within the phase field approach to simulate the interactions between induced and natural fracture yields complex structures that include fracture branching, fracture hooking etc.
The novelty of this work lies in the efficient integration of the phase-field fracture propagation models to diffusive natural fracture networks with stochastic representation of uncertainty associated with the prediction of natural fractures in a reservoir. The presented method enables practicing engineers to design hydraulic fracturing treatment accounting for the uncertainty associated with the location and spatial variations in natural fractures. Together with efficient parallel implementation, our approach allows for cost-efficient approach to optimizing production processes in the field.
Optimal well placement remains both highly challenging and significantly important in the E&P business since they impact field development decision making. Conventionally, well placement is performed manually based on well spacing, which may not capture the effect of reservoir geology effectively, especially in cases of high reservoir heterogeneities. Modern techniques tackle this problem by treating well locations as discrete optimisation problems through reservoir simulations, and thus apply heuristic algorithms to search for optimal well locations. However, these methods require considerable computational effort, which forestall any efforts at novel techniques in searching to for global optimal solutions.
This paper presents an innovative well placement optimisation workflow to minimize the calculation time of simulation using drainage volume via streamlines time-of-flight. A reservoir simulation is run for a short period of time to acquire streamlines for all proposed well locations. The time-of-flight property, along streamlines, indicates the theoretical time required for a theoretical tracer particle to move along each streamline to a producer (pressure sink). The time-of-flight, together with reservoir properties, are then used to calculate the hydrocarbon drainage volume from each producer. In which, it is the key parameter to suggest that how much hydrocarbon can move to wells with a given production period. This workflow will search for optimal well locations to maximize the hydrocarbon drainage volume with a given number of wells.
The approach translates reservoir simulation to numerical matrix union optimisation, which can be carried out at an extremely fast computational speed (less than a second for a single iteration). The expedited calculation efficiency allows exhaustive search algorithms to evaluate millions of possible well combinations and can, consequently, guarantee a global optimal solution. The workflow has been conceptually proven with a synthetic 2D simulation model, providing a pattern-like scheme to mimic the conventional approach. Furthermore, it has been successfully tested with field scale reservoir simulations. The algorithm demonstrates the advantages of optimized well-placement over conventional methods without much of an increased computational burden.
The workflow is also designed to be automated with a simple user-interaction via MATLAB and MS-Excel; namely, the SMARTDRAIN package. This allows engineers/geologists to implement it as a generic workflow without requiring extensive knowledge in mathematical algorithms. With such calculation efficiency and improved optimal solution, this approach can be applied as a new well placement optimisation standard that would add competitive value in field development planning and optimisation.
Multi-wave seismic exploration, which utilizes P-wave, S-wave and PS-wave, is an effective method for the fine exploration of petroliferous basin. The oil sands in the MacKay region are mainly lithologic reservoirs, which vary laterally. The swamp area in the surface is very big and distributed in unbalance. The near surface is complex and varied. The PS-wave residual statics is obtained by the common receiver point stack cross-correlation method controlled by PP and PS-wave structure time and the integrated global optimized static correction method. The continuity and signal to noise ratio (SNR) of seismic sections are improved obviously. The accurate imaging of PS-wave data proves the validity and reliability of the method in this paper. It provides important geophysical exploration results for the development of oil sands.
Presentation Date: Monday, October 15, 2018
Start Time: 1:50:00 PM
Location: 213A (Anaheim Convention Center)
Presentation Type: Oral
The value added by intelligent wells (I-wells) derives from real-time reservoir and production-performance monitoring together with zonal downhole flow control. Unfortunately, downhole sensors that directly measure the flow rates and phase cuts required for optimal control of the well's producing zones are not typically installed. This paper describes an "active" monitoring technique that uses a direct search method to optimize the sequence of interval-control-valve (ICV) positions during a routine multirate test in an I-well. I-well completions include both downhole flow-control tools and monitoring sensors. Reservoir inflow is controlled by means of passive devices [inflow-control devices (ICDs)], active valves (ICVs), and self-adjusting devices (autonomous ICDs and autonomous ICVs).
Basilio, Leandro (Deep Seed Solutions) | Noronha, Celso (Deep Seed Solutions) | Passos, Matheus (Deep Seed Solutions) | Calaza, Débora (Deep Seed Solutions) | da Nova, Anderson L. (Repsol Sinopec Brasil) | Daly, Roland (Repsol Sinopec Brasil)
The development of Offshore Oil and Gas Production Systems demands a multi-disciplinary team to investigate a multitude of design uncertainties extending from the reservoir to the production facilities, and commodity product sales. The generation of concept options usually follows a linear and technically oriented process, where each specialized discipline determines the technical requirements and boundary conditions to the next discipline. The process may be repeated as new information becomes available, and some design uncertainties are progressively reduced, as most of key design parameters are stochastic and non-deterministic. This methodology may vary between Oil Companies regarding the logical sequence, accuracy range and concept coverage, but is strongly driven by the Company's culture and by industrial capacity available in each region.
Due to limitations of time and availability of costly multi-disciplinary specialized resources, the generation of field development concept options is often focused primarily on generating concepts considering the highest technological maturity and the lowest Capital Expenditure (CAPEX). Following this approach, the generation of ranked concept options with a focus on the Net Present Value (NPV) is usually performed on a limited workable number of options. The NPV check is a project finance tool to assess and rank the concepts generated at the end of the engineering process. Further design phase iterations tend towards these same options, resulting in either slow and incremental improvement, or worse through propagation of the initial uncertainties, inadequate facilities design and reduced project reward.
On a process digitization perspective, an integrated and automated design computational model enables specialized disciplines to probe and visualize the concept search space, including lower maturity concepts with potentially higher NPV reward. Automation accelerates the design process, allowing the necessary time to focus efforts either on maturing concepts from an earlier phase, or on minimizing uncertainty in propagation, by reconsidering full concept search space in subsequent design phase iterations, ensuring more adequate facilities design.
The objective of this paper is to present the results of an integrated design approach applying a computationally developed model, focusing on automating the process of offshore full field concept generation and ranking during early project phases to fetch optimized designs. Based on the use of the computational model, a case study was developed, highlighting the sensitivity analysis of CAPEX, OPEX (Operational Expenditures) and NPV, by varying specific parameters in typical subsea production system architectures. The hypothetical scenario is representative of the ultra-deepwater environment in the Brazilian Pre-Salt.
Zhiyue, Wang (China University of Petroleum) | Deli, Gao (China University of Petroleum) | Jianjun, Liu (China University of Petroleum) | Degao, Hu (Sinopec Chongqing Fuling Shale Gas Exploration and Development Co., Ltd.) | Wenrui, Wu (CNPC)
Trajectory design of sidetrack horizontal well is faced with bypassing obstacles in cluster wells which makes the design process more complicated. The obstacle is assumed to be drilled trajectory sections such as vertical section and directional section, then they are described as a part of cylinder and annulus. Trajectory type for bypassing obstacle is classified based on geometrical relationship between tangent line of sidetrack point, tangent line of target point and axis of obstacle. The objective function is minimizing the total trajectory energy and the constraints include the safety distance to obstacle and the maximum build rate and maximum turn rate. Teaching-learning Cuckoo Search algorithm has been proposed to solve the established model. Case studies have been carried out according to different trajectory type, the results indicate that the proposed method can obtain sidetrack trajectory with minimum trajectory energy, in the meanwhile, the trajectory can avoid collision with obstacle and the wellbore curvatures are appropriate, the new method avoid trial calculation, it is also applicable to other types of sidetrack well trajectory for bypassing obstacles.
Sidetrack drilling in cluster wells is an effective measure to enhance oil recovery. With the increasement of wells number in cluster wells, it is more difficult to design the sidetrack horizontal well. For bypassing obstacles, the obstacle model of the vertical well and the two-dimensional directional well have been analyzed (Zhou D, 1992, Liu X, 1996), and the trajectory design methods of the the sidetrack well for bypassing obstacles also have been studied (Liu X, 2009). However, the design process of the existed method is based on horizontal projection and vertical profile. Firstly, design horizontal projection, then design vertical profile, finally, check curvature of each node of the designed trajectory. If curvatures of all nodes are no more than the allowable maximum curvature, the design is feasible, else redesign the trajectory. Obviously, the design process is complicated, it is a trial and error method and can not guarantee the designed trajectory is an optimal one.
In trajectory design of sidetrack horizontal well for bypassing obstacles, coordinates, inclination and azimuth of sidetrack point are known parameters. Besides, coordinates, inclination and azimuth of the horizontal section are also given. the obstacles are 3D horizontal wells, therefore, the obstacle model of 3D horizontal well need to be established and new design method also need to be studied to design sidetrack horizontal well for bypassing obstacles in cluster wells. Wang Z (2016) discussed the optimal design of sidetrack horizontal well.
Gramstad, Oddgeir (Schlumberger Stavanger Research (SSR)) | Øystein, Jan (Schlumberger Stavanger Research (SSR)) | Bakke, Haavig (Schlumberger Stavanger Research (SSR)) | Haukås, Jarle (Schlumberger Stavanger Research (SSR))
A new workflow for fully automated salt interpretation from seismic data has been developed and tested. In part I of the workflow, which is the focus of this paper, a DNA-inspired search algorithm is used in combination with a seeded 3D extrema surface sequence extraction method. The DNA-search algorithm is used to map seismic attribute patterns along top and base of the salt. The extrema method is then used to connect and laterally extend the DNA-detected patterns into signal consistent extrema surfaces. In part II of the workflow, the poorly defined parts of the boundary are determined by smooth surface wrapping of a seismic volume attribute that separates salt from sediments. The main objective of the workflow is to improve the quality of the salt interpretation and to make the interpretation more efficient by replacing manual work with automation. The workflow has been tested on multi-client seismic data from the Gulf of Mexico.
Presentation Date: Wednesday, September 27, 2017
Start Time: 11:00 AM
Presentation Type: ORAL
Summary Finding sparse solutions to underdetermined inverse problems has gained increased interests over the past few years. The matching pursuit (MP) algorithm is one of the most successfully deployed greedy algorithms in many applications due to its simplicity and robustness. Introduction The approximation problem using overcomplete representations and the computation of a sparse solution to the associated underdetermined linear system of equations has recently received much attention in the industry. It arises in many inverse problems such as interpolation, deghosting, demultiple, simultaneous source separation, noise attenuation and image compression (e.g., Stanton and Sacchi, 2013). Because, there is an infinite number of satisfying the above equation.
Under the Froude-scale model test environment with the scale of 1/54th, the strong Reynolds scale effects obstructs the model rotor capturing enough thrust. One effective solution is redesigning a performance matched blade to replace geometry similar blade. In this paper, the MATLAB's pattern search optimization toolbox and NREL's aerodynamic software AeroDyn V15 had been compiled together to design a blade for a 6MW floating offshore wind turbine (FOWT). And the redesigned blade was analyzed by CFD method, which had shown a reasonable agreement with the performance of prototype blade. Finally, a pitch performance mapping of the designed blade was captured preparing for future basin model test.
As a promising renewable and sustainable energy, wind energy has been made great use during the past decades. Many researching projects, especially for onshore wind turbines, had been done trying to improve its performance. And great major technology achievements have been made for onshore wind turbine, such as power capturing, noise control, which technologies are basically grasped now. Relatively, as it is known, offshore wind turbines in deep sea, would enjoy a more steady wind field without noise or visually disruption problem, which has become a promising development direction in the future wind turbine market. However, as a matter of coupled blades’ aerodynamic and hydrodynamic of platform, the floating offshore wind turbines are much more challenged in technology, comparing with the onshore wind turbine or turbines erecting on a fixed platform in shallow sea. A few related researching projects have been carrying out but there many technical obstacles still remain. When a new FOWT conception was put forward, taking a high quality basin model test would be necessary to measure its performance. As a reliable and economic method, a basin model test will capture valuable data which define the aerodynamic and hydrodynamic performance of prototype wind turbine. Specifically, conducting basin model test in a Froude environment for FOWT, whether the model blade reaches admire Froude-scaled thrust or not plays an important role when detecting response of the floating platform. However, under Froude similar environment, the rotor which is similar to prototype rotor's geometry, had shown much lower thrust at a range of working condition, and this kind of deficient had caused huge challenges to basin model test (Coulling, Goupee, Robertson., Jonkman, & Dagher, 2013). This phenomenon attributes to the immense Reynolds dissimilitude between the full scale and model scale blade turbine. More directly, for a 6MW FOWT with the scale ratio 1/54th of this paper presented, the full scale blade turbine working in a rated condition getting a Reynolds number (Re) in a range of 106~107, in contrast, the model scale blade turbine works with a Re range in 104 or lower to 103 near root area.