Editorial

Autonomous agents and computational intelligence: the future of AI application for petroleum industry

In recent years, smart energy has become an important part of smart city construction. For the oil industry, exploring how to apply enabling technologies such as data transmission, internet of things, big data analysis in the energy field can fill the gap of applying intelligent technologies to energy facilities system. For the petroleum field, one of the biggest threats to safe drilling operations during oil and gas exploration is loss circulation. In the study, based on a new assessment framework that integrates intelligent energy into smart cities, an improved fuzzy evaluation method of drilling risk based on cloud method is proposed to realize the transformation of drilling risk from quantitative to qualitative. The theory proposed in this paper considers the subjective engineering experience of experts and the information contained in objective data, which make the judgment results more objective. The cloud theory proposed can clearly show the result of risk assessment, which fully reflects the fuzziness and randomness of the drilling system. The model is embedded in the evaluation framework of smart cities, which realizes the online remote online assessment of risks by city office workers.

During drilling the mud flows through the drill string and exits through the choke valve. The mud density range has as constraints: pore pressure (minimum limit) and fracture pressure (maximum limit). In fact, annulus bottomhole (downhole) pressure needs to be higher than pore pressure, in order to prevent kick, and smaller than fracture pressure, for avoiding mud loss and formation damage. Experimental and simulation studies were implemented in order build a monitoring – diagnosing tool working together with a decision making routine based on an adaptive control scheme that employs gain scheduling. An experimental unit was built, presenting the most important characteristics of the drilling process. The transient nature of annulus bottomhole pressure is due to the process inherent disturbances: rheology, rate of penetration (ROP), kick and mud loss, which were all implemented at the experimental unit. The major objective of the present paper is using the experimental plant and a phenomenological model for implementing real time process automation, in order to assure drilling inside operational window, using in line measurements for flow and density disturbances under circulation loss and kick scenarios.

Since superconducting coils cause the current decay due to connection resistance and intrinsic characteristic in the persistent current mode, various current compensations should be required to maintain stable property in the superconducting magnet system. As an achievable solution, we fabricated a new prototype power supply, i.e., linear-type superconducting power supply (LTSPS) and we carried out the operating characteristics of LTSPS in the small scale magnets. In order to apply the LTPSP for real scale magnet, charging characteristics for various magnet scales should be expected. In the paper, based on the experimental results, we design a modeling of charging characteristic of LTSPS using a granular-based radial basis function neural networks (RBFNNs) realized with aid of the granular techniques. In contrast with the plethora of existing approaches, here we consider a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based RBFNN is based on two categories of methods such as K-means clustering and the fuzzy C-means (FCM) clustering method. As shown in the experimental studies, the granular-based RBFNN possesses essential properties of universal approximation. The model of charging characteristic of LTSPS constructed is concerned with a limited dataset (where the limitations are associated with high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. For modeling and evaluation of the performance of the SPS neural network, in advance we also run experiments for other publicly available data such as a well-known NASA software project data as well as synthetic nonlinear data.

Lost circulation is the most common problem encountered while drilling oil wells. This paper describes a distributed fuzzy expert system, called Smart-Drill, aimed in helping petroleum engineers to diagnose and solve lost circulation problems. To represent and manipulate perception-based evaluations of uncertainties of facts and rules, the expert system uses an uncertainty model with qualitative scales of plausibility values and multi-set-based fuzzy algebra of strict monotonic operations. Its realization in inference procedures permits taking into account the change of plausibility of premises in expert systems rules. Original tools like CAPNET Expert System Shell, Knowledge Acquisition Tool and WITSML Converter implementing the proposed model were used for the development of the Smart-Drill. Overall, the system architecture is discussed and implementation details are provided. Both desktop and Web-based implementations permit petroleum engineers benefit from the system working out in the field. The system is currently at field testing phase in PEMEX, Mexican Oil Company.