Subsurface analytics: Contribution of artificial intelligence and machine learning to reservoir engineering, reservoir modeling, and reservoir management

Traditional Numerical Reservoir Simulation has been contributing to the oil and gas industry for decades.The current state of this technology is the result of decades of research and development by a large number of engineers and scientists.Starting in the late 1960s and early 1970s,advances in computer hardware along with development and adaptation of clever algorithms resulted in a paradigm shift in reservoir studies moving them from simplified analogs and analytical solution methods to more mathematically robust computational and numerical solution models.

Evaluation of reservoir environment by chemical properties of reservoir water‒A case study of Chang 6 reservoir in Ansai oilfield,Ordos Basin,China

The Ordos Basin is the largest continental multi-energy mineral basin in China,which is rich in coal,oil and gas,and uranium resources.The exploitation of mineral resources is closely related to reservoir water.The chemical properties of reservoir water are very important for reservoir evaluation and are significant indicators of the sealing of reservoir oil and gas resources.Therefore,the caprock of the Chang 6 reservoir in the Yanchang Formation was evaluated.The authors tested and analyzed the chemical characteristics of water samples selected from 30 wells in the Chang 6 reservoir of Ansai Oilfield in the Ordos Basin.The results show that the Chang 6 reservoir water in Ansai Oilfield is dominated by calcium-chloride water type with a sodium chloride coefficient of generally less than 0.5.The chloride magnesium coefficients are between 33.7 and 925.5,most of which are greater than 200.The desulfurization coefficients range from 0.21 to 13.4,with an average of 2.227.The carbonate balance coefficients are mainly concentrated below 0.01,with an average of 0.008.The calcium and magnesium coefficients are between 0.08 and 0.003,with an average of 0.01.Combined with the characteristics of the four-corner layout of the reservoir water,the above results show that the graphics are basically consistent.The study indicates that the Chang 6 reservoir in Ansai Oilfield in the Ordos Basin is a favorable block for oil and gas storage with good sealing properties,great preservation conditions of oil and gas,and high pore connectivity.

Intelligent Petroleum Engineering

Data-driven approaches and artificial intelligence(AI)algorithms are promising enough to be relied on even more than physics-based methods;their main feed is data which is the fundamental element of each phenomenon.These algorithms learn from data and unveil unseen patterns out of it The petroleum industry as a realm where huge volumes of data are generated every second is of great interest to this new technology.As the oil and gas industry is in the transition phase to oilfield digitization,there has been an increased drive to integrate data-driven modeling and machine learning(ML)algorithms in different petroleum engineering challenges.ML has been widely used in different areas of the industry.Many extensive studies have been devoted to exploring AI applicability in various disciplines of this industry;however,lack of two main features is noticeable.Most of the research is either not practical enough to be applicable in real-field challenges or limited to a specific problem and not generalizable.Attention must be given to data itself and the way it is classified and stored.Although there are sheer volumes of data coming from different disciplines,they reside in departmental silos and are not accessible by consumers.In order to derive as much insight as possible out of data,the data needs to be stored in a centralized repository from where the data can be readily consumed by different applications.

Application and development trend of artificial intelligence in petroleum exploration and development

Aiming at the actual demands of petroleum exploration and development,this paper describes the research progress and application of artificial intelligence(AI)in petroleum exploration and development,and discusses the applications and development directions of AI in the future.Machine learning has been preliminarily applied in lithology identification,logging curve reconstruction,reservoir parameter estimation,and other logging processing and interpretation,exhibiting great potential.Computer vision is effective in picking of seismic first breaks,fault identification,and other seismic processing and interpretation.Deep learning and optimization technology have been applied to reservoir engineering,and realized the real-time optimization of waterflooding development and prediction of oil and gas production.The application of data mining in drilling,completion,and surface facility engineering etc.has resulted in intelligent equipment and integrated software.The potential development directions of artificial intelligence in petroleum exploration and development are intelligent production equipment,automatic processing and interpretation,and professional software platform.The highlights of development will be digital basins,fast intelligent imaging logging tools,intelligent seismic nodal acquisition systems,intelligent rotary-steering drilling,intelligent fracturing technology and equipment,real-time monitoring and control of zonal injection and production.

Analytical models & type-curve matching techniques for reservoir characterization using wellbore storage dominated flow regime

The applicability of early time data in reservoir characterization is not always considered worthy.Early time data is usually controlled by wellbore storage effect.This effect may last for pseudo-radial flow or even boundary dominated flow.Eliminating this effect is an option for restoring real data.Using the data with this effect is another option that could be used successfully for reservoir characterization.This paper introduces new techniques for restoring disrupted data by wellbore storage at early time production.The proposed techniques are applicable for reservoirs depleted by horizontal wells and hydraulic fractures.Several analytical models describe early time data,controlled by wellbore storage effect,have been generated for both horizontal wells and horizontal wells intersecting multiple hydraulic fractures.The relationships of the peak points(humps)with the pressure,pressure derivative and production time have been mathematically formulated in this study for different wellbore storage coefficients.For horizontal wells,a complete set of type curves has been included for different wellbore lengths,skin factors and wellbore storage coefficients.Another complete set of type curves has been established for fractured formations based on the number of hydraulic fractures,spacing between fractures,and wellbore storage coefficient.The study has shown that early radial flow for short to moderate horizontal wells is the most affected by wellbore storage while for long horizontal wells;early linear flow is the most affected flow regime by wellbore storage effect.The study has also emphasized the applicability of early time data for characterizing the formations even though they could be controlled by wellbore storage effect.As a matter of fact,this paper has found out that wellbore storage dominated flow could have remarkable relationships with the other flow regimes might be developed during the entire production times.These relationships can be used to properly describe the formations and quantify some of their characteristics.

Reservoir-engineered entanglement in an unresolved-sideband optomechanical system

We study theoretically the generation of strong entanglement of two mechanical oscillators in an unresolved-sideband optomechanical cavity,using a reservoir engineering approach.In our proposal,the effect of unwanted counter-rotating terms is suppressed via destructive quantum interference by the optical field of two auxiliary cavities.For arbitrary values of the optomechanical interaction,the entanglement is obtained numerically.In the weak-coupling regime,we derive an analytical expression for the entanglement of the two mechanical oscillators based on an effective master equation,and obtain the optimal parameters to achieve strong entanglement.Our analytical results are in accord with numerical simulations.