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Abstract Suitable drilling fluid with suitable properties is a critical factor in successful drilling and completion operations. Drilling fluids consultant will need information such as formation lithology, wellbore geometry, temperature, pressure to be able to formulate fluids for different sections. Drilling fluids consultants' job is to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections. The paper describes a drilling fluid advisory system with demonstrating examples to the end user. The advisory system includes a Bayesian decision network (BDN) model that utilize inputs such as temperature and pressure and provide drilling fluids formulations based on Bayesian probability determinations. A number of drilling fluid specialists/experts feedback were gathered to develop the drilling fluid advisory system. The drilling fluid specialists/experts feedback were used to design Bayesian Network Model that would allow the end-user to take an elementary data set to obtain optimum recommendations in the area of drilling fluids.
Abstract Texas A&M University recently has established a new method to develop a drilling expert system that can be used as a training tool for young engineers or as a consultation system in various drilling engineering concepts such as drilling and completion fluids, cementing, completion, and underbalanced drilling practices. To the best of the authors knowledge there is no standards developed to aid drilling engineers and scientists to formulate effective drilling fluids systems for the entire well sections. The objective of this paper is to set a module that should aid drilling engineers when designing drilling fluids. A module was created based on several inputs. To create this module, we interviewed experts to gather the information required to determine best practices as a function of different probabilities. Drilling fluids formulations were gathered from Saudi Arabia fields to build up this model. The Bayesian approach was found suitable for designing expert system. The model can work as a guide to aid drilling engineers and scientists to design and execute optimum drilling fluids. Using this approach to build up expert systems is more flexible than using flow charts. Updating flow charts is time consuming and require redesigning them again to be used by different experts or in different fields. Using Bayesian network allows us to update our industry practices by updating the probabilities states mentioned in this paper.
Abstract Bad quality data is ubiquitous in oil well drilling operations. The hostile and uncertain environment in which sensors are used and the lower priority/importance usually given to regular calibration of these sensors to ensure accuracy are the main reasons for the lack of good quality and reliable data. Today, online streaming of sensor data from at least all the primary sensors on a rig to a real-time data monitoring facility is becoming commonplace. There also exists software to analyze this data, in real-time, to detect trends and identify potential drilling problems long before they occur. However, these software applications require good quality data to perform accurate analysis. When data is not validated, false and missed alarms are the norm whereby supposedly autonomous software applications require continuous human supervision. High quality data will also be required in the near future when adopting control algorithms for rig automation, which will use this data for making autonomous, closed-loop decisions. In this paper, a novel technique for real-time sensor data validation is proposed that improves the quality of data collected from the sensors on a rig. This technique uses a model-based approach and the principle of conditional independence. Here, a sensor probabilistic graphical model is created and the relational redundancies in the model are exploited to differentiate between a sensor fault and a process fault as well as to identify the faulty sensor. The approach described in this paper allows for model update in real-time to account for process degradation/changes, thereby alleviating problems associated with the use of a purely analytical model. The application of the algorithm on a generated data set and the results of the experiment are presented in this paper. The adoption of such techniques is expected to greatly enhance the safety and the performance of well drilling and pave the path forward for more automated rig operations.
Abstract To the best of the authors' knowledge, there are no standard guidelines to help in the effective design of completion practices. Many completions have been analyzed in this project, resulting in the best practices, as outlined in this paper. The objective of this paper is to propose a set of guidelines for the optimal completion design, by integrating current best practices through a decision-making system based on Artificial Bayesian Intelligence. Best completion practices collected from data, models, and experts' opinions, are integrated into a Bayesian Network BN to simulate likely scenarios of its use, that will honor efficient designs when dictated by varying well objectives, well types, temperatures, pressures, rock and fluid properties. The described decision-making model follows a causal and uncertainty-based approach capable of simulating realistic conditions on the use of completion operations. For instance, the use of water swelling packer dictates the use of organic acids instead of HCl acids. However, rock type and well geometry affect our selection of treatment fluids. Another example is selection of sand control method based on rock properties. The paper also outlines best operational practices in fracturing, sand control, perforation, treatment and completion fluids, multilateral junction level selection and lateral completion. The paper also discusses in details special well completions. Completion experts' opinions were considered in building the model in this paper. The outcome of this paper is a user-friendly tool, where one can easily find the specific subject of interest, and by the click of a button, get the related information you are seeking. Field cases will also be discussed to validate this work.
This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 187447, “Challenges and Lessons From Implementing a Real-Time Drilling Advisory System,” by Benjamin J. Spivey, SPE, Gregory S. Payette, SPE, and Lei Wang, SPE, ExxonMobil Upstream Research Company; Jeffrey R. Bailey, SPE, ExxonMobil Development Company; Derek Sanderson, XTO Energy; and Stephen W. Lai, SPE, Behtash Charkhand, SPE, and Aaron Eddy, SPE, Pason Systems, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 9–11 October. The paper has not been peer reviewed.
This paper discusses the technical challenges related to implementing a rigsite, real-time drilling advisory system and current solutions to these challenges. The system uses a data-driven response-surface model based on physics-based calculations to optimize rate of penetration (ROP) while minimizing drilling-vibration dysfunction with regard to lateral (whirl) and torsional (stick/slip) vibrational modes. Minimizing these vibrations is important to mitigate bit damage that can lead to reduced ROP and increased bit trips.
The system is a rigsite software application that should be deployed in view of the driller. Fig. 1 shows a driller-cabin deployment.
The software contains capabilities for real-time surface drilling-data acquisition, drilling-performance estimation, vibration analysis, surface trends for drilling performance, and drill- off-test guidance for drilling optimization. The system primarily serves as an open-loop advisory tool but retains capabilities for closed-loop autodriller and topdrive control. The user interface provides the rigsite personnel with drilling-performance surface trends (e.g., ROP, drilling efficiency, and stick/slip vibration), bit aggressiveness and depth-of-cut (DOC) calculations, and drilling-parameter set-point recommendations on the basis of the surface trends.
Data Input and Output. The system operates on 1-second data provided from the electronic data-recording equipment. Input data consist of data channels included among standard or spare Well Information Transfer Specification (WITS) Record 1 items—block height, weight on bit (WOB), rotary speed, mud-flow rate, hole depth, bit depth, torque, and differential pressure.
Drilling-Performance Estimation. The system filters and preprocesses the raw WITS Record 1 data to calculate the drilling performance variables: ROP, surface mechanical specific energy (MSE), motor MSE, DOC divided by WOB, torsional-severity estimate (TSE), bit aggressiveness, and DOC.
Performance Averaging and Modeling. The measures of drilling performance, primarily the ROP, drilling efficiency, and stick/slip indicator, are averaged over depth drilled to produce a mean or median value referred to as a “response point.” A clustering algorithm groups these response points in the 2D drilling-parameter space. The response-point groups, or “calibration points,” serve as estimates of the drilling set points to measure whether the drilling-parameter space is explored sufficiently to produce an accurate response-surface model.
Optimization. An objective function merges the response surfaces of multiple performance objectives into a single objective surface. This objective function makes a tradeoff between the ROP, drilling efficiency, and stick/slip terms.