Acid Effect Life Cycle in Carbonate Reservoir, Field Case

Al-Marzouqi, Mariam; Alfarisi, Omar; Al-Felasi, Ali; Amer, Mohamed Mostafa; Budebes, Sultan; Yammahi, Fatmah

doi:10.2118/138523-ms

Cited by 4 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Fig. 1, the O&G value chain we use in this research shows the impact sequence of each of the seven stages with Artificial intelligence (AI) optimization: Geoscience , Reservoir [39][40][41][42][43][44][45][46], Completion [47], Drilling [48], Production, Network, and Facility. Where integrated optimization with AEGI provide high efficiency in term of time, cost [49], environment, and energy production.…”

Section: Introductionmentioning

confidence: 99%

Artificial Energy General Intelligence AEGI

Alfarisi¹

2023

Preprint

View full text Add to dashboard Cite

Artificial Energy General Intelligence (AEGI) is a natural progression of Artificial General Intelligence (AGI) that caters to the energy industry. It is crucial to optimize the entire value chain involved in generating, transporting, and storing energy for the betterment of humanity, the environment, industry, and the scientific community. Most research efforts focus on a specific area of the value chain, leading to a disconnect between multiple disciplines and hindering effective problem-solving. AEGI proposes integrating the learning from each discipline in the energy sector to create an optimal solution that simultaneously addresses multiple objectives. This integration is more complex than solving each discipline's challenges separately, but achieving a sustainable and efficient energy system is necessary.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Energy General Intelligence AEGI

Alfarisi¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In Fig. 2, we show the value chain example of one sector of Oil and Gas (O&G) with artificial intelligence (AI) optimization: Geoscience , Reservoir [47][48][49][50][51][52][53][54], Completion [55], Drilling [56], Production, Network, and Facility. O&G integrated optimization with Artificial Energy General Intelligence AEGI provides high efficiency in terms of time, cost [57], environment, and energy production.…”

Section: Introductionmentioning

confidence: 99%

Artificial Earth Economics General Intelligence AEEGI

Alfarisi

2023

Preprint

View full text Add to dashboard Cite

Artificial Earth Economics General Intelligence (AEEGI) is a natural progression of Artificial General Intelligence (AGI) that caters to the Economics of the Earth caring. It is crucial to optimize the entire spectrum of generating, transporting, storing, and consuming earth resources for the betterment of humanity, the environment, industry, and the scientific community. Most research efforts focus on a specific sector, leading to a disconnect between multiple disciplines and hindering effective problem-solving while ignoring the economic impact on the whole ecosystem, including people. AEEGI proposes creating a new coin specific to earth economics and integrating the positive initiative and outcomes from each sector to create an optimal solution that simultaneously addresses multiple objectives that generate the required coins (cash) to achieve them; we call this coin the Hubnomics Earth Wise Coin (EarthYzcoin). The generation of EarthYzcoin and integration across all industry sectors and its value chain are more complex than solving each industry sector or specific value chain challenges separately, but achieving a sustainable and efficient industry-environment-economic system is necessary. Because the industry, as per the current economic system, would have to invest more to become more environmentally friendly. Therefore, with EarthYzcoin, the required investment would be generated by EarthYzcoin and given to industries and universities to research and develop cleaner processes and technologies. At the same time, as per the definition of Yzcoin, the fund that Yzcoin generates is not taken from anyone; instead, it is purely generated as an equivalent value to what the environmental project deliverables outcome. However, EarthYzcoin is an upfront generation of value provided to institutions that would work on researching and developing cleaner solutions to industries and life. The role of AEEGI is to ensure continuous learning from every new outcome to keep optimizing.

show abstract

New Approach of Using Wells Static Pressure Measurements in Identifying Dynamic Regions and Sectors in a Giant Carbonate Reservoir

Sadok¹,

Baslaib²,

Huang³

2015

Day 4 Thu, November 12, 2015

View full text Add to dashboard Cite

Compartmentalization (vertical and lateral) is often a major uncertainty at the field appraisal stage, impacting important investment decisions. Unfortunately the most definitive compartmentalization data (dynamic production data or high resolution 4D seismic) are not usually available so early in field life. Usually geometrically equal sectors are defined to start with to be used as a guide to monitor and manage the reservoir. In mature reservoirs, these data are available. The understanding of the different zones and compartments boundaries is crucial for better re-sectorization and better effective management strategies especially in case of the giant field with gas and water injection schemes. This paper illustrates how indications of compartmentalization and limits of sectors can be achieved by analyzing the sporadic pressure static data dispersed in time and space. The first step of this work is to integrate seismic re-interpretation of the faults with Bottom-Hole Static Pressure (BHSP) analysis to identify acting faulting and barriers. This is achieved by overlapping all isobar maps and then by taking the derivatives of the pressure versus x and y directions. The pressure contrast in space can be highlighted based on a given threshold. If this pressure contrast is overlapped by a seismically identified fault, it can be considered as a barriers or semi-barrier or just absent based on the degree of pressure contrast. The second step is to use the identified boundaries as guidance to limit and confine the different zones and fault blocks that are suspected to act more or less as isolated units. After identifying the boundaries more or less sealing a classification of the static pressure measurements using k-mean value will help to identify the dynamic regions that behave in the same way. A re-sectorization of the field will be based on those units. A forward calculation of voidage replacement ratio and injection production versus pressure can be used as a criterion for identifying the best sectoring scenario.

show abstract

Acid Effect Life Cycle in Carbonate Reservoir, Field Case

Cited by 4 publications

References 0 publications

Artificial Energy General Intelligence AEGI

Artificial Energy General Intelligence AEGI

Artificial Earth Economics General Intelligence AEEGI

New Approach of Using Wells Static Pressure Measurements in Identifying Dynamic Regions and Sectors in a Giant Carbonate Reservoir

Contact Info

Product

Resources

About